This repo contains a list of papers for physiological signal classification using machine learning/deep learning.
If you have any suggested papers, please contact me ziyujia{at}bjtu.edu.cn
We conduct the overall statistical analysis of all papers in this list. Here we give the number of papers for each task, the number of papers of each model type, and the proportion of papers of signal type.
| Task/Application | Title | Model | Publication | Year |
|---|---|---|---|---|
| Motor Imagery | MMCNN: A Multi-branch Multi-scale Convolutional Neural Network for Motor Imagery Classification | CNN | ECML PKDD | 2021 |
| Motor Imagery | Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network | CNN | Expert Syst Appl | 2020 |
| Motor Imagery | Making sense of spatio-temporal preserving representations for EEG-based human intention recognition | CNN, LSTM | IEEE Trans. Cybern. | 2020 |
| Motor Imagery | A deep CNN approach to decode motor preparation of upper limbs from time–frequency maps of EEG signals at source level | CNN (CWT,TF) | Neural Networks | 2020 |
| Motor Imagery | Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system | SVM | Applied Sciences | 2020 |
| Motor Imagery | A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning | SCNN (CWT) | Applied Sciences | 2020 |
| Motor Imagery | HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification | CNN | J. Neural. Eng. | 2020 |
| Motor Imagery | Learning Space-Time-Frequency Representation with Two-Stream Attention Based 3D Network for Motor Imagery Classification | CNN | IEEE ICDM | 2020 |
| Motor Imagery | Application of continuous wavelet transform and convolutional neural network in decoding motor imagery brain-computer interface | CNN (CWT) | Entropy | 2019 |
| Motor Imagery | Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion | CNN | Future Gener Comput Syst | 2019 |
| Motor Imagery | A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals. | GDL | Expert Systems | 2019 |
| Motor Imagery | Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Motor Imagery | A parallel multiscale filter bank convolutional neural networks for motor imagery EEG classification. | CNN | Front. Neurosci. | 2019 |
| Motor Imagery | Subject-independent brain–computer interfaces based on deep convolutional neural networks. | CNN (SSFR) | IEEE Trans. Neural Netw. Learn. Syst. | 2019 |
| Motor Imagery | A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network | CNN (CWT) | Sensors | 2019 |
| Motor Imagery | A multi-branch 3D convolutional neural network for EEG-based motor imagery classification. | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Motor Imagery | A novel hybrid deep learning scheme for four-class motor imagery classification. | CNN, LSTM (FBCSP) | J Neural Eng | 2019 |
| Motor Imagery | An advanced bispectrum features for EEG-based motor imagery classification. | SVM (VSBS) | Expert Systems with Applications | 2019 |
| Motor Imagery | Densely feature fusion based on convolutional neural networks for motor imagery EEG classification | CNN (DFFN) | IEEE Access | 2019 |
| Motor Imagery | A deep learning framework for decoding motor imagery tasks of the same hand using eeg signals | CNN (QTFD) | IEEE Access | 2019 |
| Motor Imagery | A deep transfer convolutional neural network framework for EEG signal classification. | CNN | IEEE Access | 2019 |
| Motor Imagery | A channel-projection mixed-scale convolutional neural network for motor imagery EEG decoding | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Motor Imagery | Learning joint space–time–frequency features for EEG decoding on small labeled data. | CNN | Neural Networks | 2019 |
| Motor Imagery | Motor imagery EEG classification using capsule networks | CNN (STFT) | Sensors | 2019 |
| Motor Imagery | Semisupervised deep stacking network with adaptive learning rate strategy for motor imagery EEG recognition. | SADSN | Neural computation | 2019 |
| Motor Imagery | Efficient classification of motor imagery electroencephalography signals using deep learning methods. | CNN | Sensors | 2019 |
| Motor Imagery | Separated channel convolutional neural network to realize the training free motor imagery BCI systems | SCNN(CSP) | Biomed Signal Process Control | 2019 |
| Motor Imagery | A convolutional recurrent attention model for subject-independent eeg signal analysis. | CNN, RNN (CRAM) | IEEE Signal Process. Lett. | 2019 |
| Motor Imagery | Classification of multiple motor imagery using deep convolutional neural networks and spatial filters | CNN (DFBCSP) | Appl Soft Comput | 2019 |
| Motor Imagery | Convolutional neural network based approach towards motor imagery tasks EEG signals classification | CNN (STFT,CWT) | IEEE Sensors J. | 2019 |
| Motor Imagery | Domain adaptation with source selection for motor-imagery based BCI | CNN (PSD) | BCI | 2019 |
| Motor Imagery | Validating deep neural networks for online decoding of motor imagery movements from EEG signals. | LSTM, CNN | Sensors | 2019 |
| Motor Imagery | Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification | CNN | IEEE Access | 2019 |
| Motor Imagery | A novel deep learning approach with data augmentation to classify motor imagery signals. | CNN, WNN | IEEE Access | 2019 |
| Motor Imagery | EEG classification of motor imagery using a novel deep learning framework | CNN (STFT) | Sensors | 2019 |
| Motor Imagery | Walking imagery evaluation in brain computer interfaces via a multi-view multi-level deep polynomial network. | MMDPN (CSP, PSD, WPT) | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Motor Imagery | Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. | MFF | IEEE Comput Intell M | 2019 |
| Motor Imagery | Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning | CNN | Sensors | 2019 |
| Motor Imagery | Deep learning with EEG spectrograms in rapid eye movement behavior disorder | CNN | Front. Neurol. | 2019 |
| Motor Imagery | Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. | CNN | IEEE Access | 2018 |
| Motor Imagery | An end-to-end deep learning approach to MI-EEG signal classification for BCIs. | CNN | Expert Syst Appl | 2018 |
| Motor Imagery | Deep fusion feature learning network for MI-EEG classification. | CNN, LSTM (DWT) | IEEE Access | 2018 |
| Motor Imagery | LSTM-based EEG classification in motor imagery tasks. | LSTM | IEEE Trans. Neural Syst. Rehabil. Eng. | 2018 |
| Motor Imagery | EEG classification using sparse Bayesian extreme learning machine for brain–computer interface. | SBELM | Neural Comput Appl | 2018 |
| Motor Imagery | Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. | LSTM, GRU (FBCSP) | BMC bioinformatics | 2018 |
| Motor Imagery | A hierarchical semi-supervised extreme learning machine method for EEG recognition. | HSS-ELM | Med. Biol. Eng. Comput. | 2018 |
| Motor Imagery | EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. | CNN | J Neural Eng | 2018 |
| Motor Imagery | The motor imagination EEG recognition combined with convolution neural network and gated recurrent unit. | CNN, GRU | CCC | 2018 |
| Motor Imagery | A Deep Convolutional Neural Network Based Classification Of Multi-Class Motor Imagery With Improved Generalization. | CNN | EMBC | 2018 |
| Motor Imagery | Temporally constrained sparse group spatial patterns for motor imagery BCI. | SVM (TSGSP) | IEEE Trans. Cybern. | 2018 |
| Motor Imagery | Classification of multi-class BCI data by common spatial pattern and fuzzy system | FLS | IEEE Access | 2018 |
| Motor Imagery | Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. | MKELM | Expert Syst Appl | 2018 |
| Motor Imagery | Learning temporal information for brain-computer interface using convolutional neural networks. | CNN (FBCSP) | IEEE Trans. Neural Netw. Learn. Syst. | 2018 |
| Motor Imagery | Deep recurrent spatio-temporal neural network for motor imagery based BCI. | CNN, RNN | BCI | 2018 |
| Motor Imagery | Classification of motor imagery for Ear-EEG based brain-computer interface. | CSP | BCI | 2018 |
| Motor Imagery | A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image. | CNN (CWT) | ICOIN | 2018 |
| Motor Imagery | Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2018 |
| Motor Imagery | Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks | LSTM | IEEE EMBC | 2018 |
| Motor Imagery | Deep convolutional neural network for decoding motor imagery based brain computer interface. | CNN (STFT) | ICSPCC | 2017 |
| Motor Imagery | Deep learning with convolutional neural networks for EEG decoding and visualization. | CNN | Human brain mapping | 2017 |
| Motor Imagery | EEG feature extraction and classification in multiclass multiuser motor imagery brain computer interface u sing Bayesian Network and ANN. | BN | ICICICT | 2017 |
| Motor Imagery | A deep learning scheme for motor imagery classification based on restricted boltzmann machines | CNN | Comput. Biol. Med. | 2017 |
| Motor Imagery | A deep learning approach for motor imagery EEG signal classification. | CSP | APWC on CSE | 2016 |
| Motor Imagery | A novel deep learning approach for classification of EEG motor imagery signals. | CNN (SAE) | J Neural Eng | 2016 |
| Motor Imagery | A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. | RBM (FFT, WPD) | IEEE Trans. Neural Syst. Rehabil. Eng. | 2016 |
| Motor Imagery | A multi-label classification method for detection of combined motor imageries. | CSP | IEEE Trans. Syst., Man, Cybern. Syst. | 2015 |
| Motor Imagery | On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. | CNN (FCMS) | EMBC | 2015 |
| Motor Imagery | Parallel convolutional-linear neural network for motor imagery classification. | CNN | EUSIPCO | 2015 |
| Motor Imagery | Increase performance of four-class classification for motor-imagery based brain-computer interface. | CSP | CITS | 2014 |
| Motor Imagery | A novel classification method for motor imagery based on Brain-Computer Interface. | LDA (CSP) | IJCNN | 2014 |
| Motor Imagery | Neural network-based three-class motor imagery classification using time-domain features for BCI applications. | MLP, RBF | IEEE TENSYMP | 2014 |
| Motor Imagery | Deep learning of multifractal attributes from motor imagery induced eeg | DBN | IEEE ICME | 2014 |
| Motor Imagery | A deep learning method for classification of eeg data based on motor imagery | DBN, SVM | Sci. World J. | 2014 |
| Motor Imagery | EEG feature comparison and classification of simple and compound limb motor imagery. | CSP (SVM) | J Neuroeng Rehabil | 2013 |
| Motor Imagery | Evolving spatial and frequency selection filters for brain-computer interfaces. | CSP | IEEE Trans. Evol. Comput. | 2010 |
| Motor Imagery | Convolutional neural network with embedded fourier transform for eeg classification | CNN | IEEE MLSP | 2008 |
| Sleep stage classification | Metasleeplearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning | MAML | IEEE J. Biomed. Health Inform. | 2020 |
| Sleep stage classification | A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from EEG signals | CNN | IEEE Trans. Circuits Syst., II, Exp. Briefs | 2020 |
| Sleep stage classification | inySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG | CNN, LSTM | IEEE EMBC | 2020 |
| Sleep stage classification | Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study | CNN, LSTM | IEEE EMBC | 2020 |
| Sleep stage classification | Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network | CNN | IEEE EMBC | 2020 |
| Sleep stage classification | Personalized automatic sleep staging with single-night data: a pilot study with Kullback--Leibler divergence regularization | RNN | Physiological measurement | 2020 |
| Sleep stage classification | Intra-and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG | CNN, RNN | Biomed Signal Process Control | 2020 |
| Sleep stage classification | Automatic identification of insomnia based on single-channel EEG labelled with sleep stage annotations | CNN | IEEE Access | 2020 |
| Sleep stage classification | Automatic sleep stage classification with single channel EEG signal based on two-layer stacked ensemble model | RF,LGB | IEEE Access | 2020 |
| Sleep stage classification | Sleep stage classification model based ondeep convolutional neural network | CNN | J. Zhejiang Univ. Eng. | 2020 |
| Sleep stage classification | Automatic Sleep Staging Based on XGBOOST Physiological Signals | XGBOOST | ICMIC | 2019 |
| Sleep stage classification | Diffuse to fuse EEG spectra--intrinsic geometry of sleep dynamics for classification | Diffusion Map | Biomed Signal Process Control | 2019 |
| Sleep stage classification | An image based prediction model for sleep stage identification | CNN | ICIP | 2019 |
| Sleep stage classification | Deep convolutional neural network for classification of sleep stages from single-channel EEG signals | CNN | J. Neurosci. Methods | 2019 |
| Sleep stage classification | A Novel Sleep Staging Algorithm Based on Hybrid Neural Network | CNN, BiLSTM | ICEIEC | 2019 |
| Sleep stage classification | Pediatric sleep stage classification using multi-domain hybrid neural networks | CNN, BiLSTM | IEEE Access | 2019 |
| Sleep stage classification | Convolutional neural networks for sleep stage scoring on a two-channel EEG signal | CNN | Soft Computing | 2019 |
| Sleep stage classification | End-to-end sleep staging with raw single channel EEG using deep residual convnets | CNN, LSTM | IEEE EMBS BHI | 2019 |
| Sleep stage classification | SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach | CNN, BiLSTM | PloS one | 2019 |
| Sleep stage classification | utomatic sleep staging employing convolutional neural networks and cortical connectivity images | CNN | IEEE Trans. Neural Netw. Learn. Syst | 2019 |
| Sleep stage classification | Investigating the effect of short term responsive VNS therapy on sleep quality using automatic sleep staging | SVM | IEEE EMBC | 2019 |
| Sleep stage classification | Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals | LSTM | Comput. Biol. Med. | 2019 |
| Sleep stage classification | U-time: A fully convolutional network for time series segmentation applied to sleep staging | U-Net | NIPS | 2019 |
| Sleep stage classification | DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal | CNN | J. Neurosci. Methods | 2019 |
| Sleep stage classification | Driving fatigue detection from EEG using a modified PCANet method | PCANet, SVM | Comput. Intell. Neurosci. | 2019 |
| Sleep stage classification | Detecting abnormal electroencephalograms using deep convolutional networks | CNN | Clin. Neurophysiology | 2019 |
| Sleep stage classification | A deep learning approach for real-time detection of sleep spindles | CNN + RNN | J. Neural Eng. | 2019 |
| Sleep stage classification | SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach | CNN + RNN | PLoS ONE | 2019 |
| Sleep stage classification | Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG | CNN | Comput. Methods Programs Biomed | 2019 |
| Sleep stage classification | Sleep Stage Classification Based on EEG Signal by Using EMD and DFA Algorithm | EMD ,DFA | ICRAI | 2018 |
| Sleep stage classification | Automatic sleep stage classification using single-channel eeg: Learning sequential features with attention-based recurrent neural networks | A-BiGRU, SVM | IEEE EMBC | 2018 |
| Sleep stage classification | Recurrent deep neural networks for real-time sleep stage classification from single channel EEG | CNN, LSTM | Front Comput Neurosc | 2018 |
| Sleep stage classification | Deep convolutional network method for automatic sleep stage classification based on neurophysiological signals | CNN, LSTM | CISP-BMEI | 2018 |
| Sleep stage classification | DNN filter bank improves 1-max pooling CNN for single-channel EEG automatic sleep stage classification | DNN, CNN | IEEE EMBC | 2018 |
| Sleep stage classification | Complex-valued unsupervised convolutional neural networks for sleep stage classification | CNN | Comput Meth Prog Bio | 2018 |
| Sleep stage classification | An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank | SVM | Comput. Biol. Med. | 2018 |
| Sleep stage classification | A convolutional neural network for sleep stage scoring from raw single-channel EEG | CNN | Biomed Signal Process Control | 2018 |
| Sleep stage classification | ResSleepNet: Automatic sleep stage classification on raw single-channel EEG | ResNet | MSE | 2018 |
| Sleep stage classification | Complex-valued unsupervised convolutional neural networks for sleep stage classification | CU-CNN | Comput. Methods Programs Biomed. | 2018 |
| Sleep stage classification | Neonatal sleep state identification using deep learning autoencoders | GRU | IEEE EMBC | 2018 |
| Sleep stage classification | Automatic human sleep stage scoring using deep neural networks | CNN, LSTM | Front. Neurosci. | 2018 |
| Sleep stage classification | A novel multi-class EEG-based sleep stage classification system | RF | IEEE Trans. Neural Syst. Rehabil. Eng. | 2017 |
| Sleep stage classification | Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring | CNN | IEEE MLSP | 2017 |
| Sleep stage classification | A new method for automatic sleep stage classification | FDCCNN | IEEE Trans. Biomed. Circuits Syst. | 2017 |
| Sleep stage classification | SLEEPNET: automated sleep staging system via deep learning | CNN, RNN | arXiv | 2017 |
| Sleep stage classification | A decision support system for automated identification of sleep stages from single-channel EEG signals | Bagging | Knowledge-Based Systems | 2017 |
| Sleep stage classification | DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG | CNN, BiLSTM | IEEE Trans. Neural Syst. Rehabil. Eng. | 2017 |
| Sleep stage classification | Deep learning and insomnia: assisting clinicians with their diagnosis | DNN | IEEE J. Biomed. Health Inform. | 2017 |
| Sleep stage classification | Learning sleep stages from radio signals: A conditional adversarial architecture | CNN, RNN | ICML | 2017 |
| Sleep stage classification | Time-Frequency Convolutional Neural Network for Automatic Sleep Stage Classification Based on Single-Channel EEG | CNN | IEEE ICTAI | 2017 |
| Sleep stage classification | Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain | RF | Med Biol Eng Comput | 2016 |
| Sleep stage classification | EEG sleep stages classification based on time domain features and structural graph similarity | SGSKM | IEEE Trans. Neural Syst. Rehabil. Eng. | 2016 |
| Sleep stage classification | Nonlinear dynamics measures for automated EEG-based sleep stage detection | NDM | Eur Neurol | 2016 |
| Sleep stage classification | Automatic classification of sleep stages based on the time-frequency image of EEG signals | MC-LS-SVM | Comput Methods Programs Biomed | 2013 |
| Sleep stage classification | Analysis and automatic identification of sleep stages using higher order spectra | GMM | Int J Neural Syst | 2010 |
| Brain functionality classification | Is it possible to detect cerebral dominance via EEG signals by using deep learning? | CNN, SVM | Med. Hypotheses | 2019 |
| Brain functionality classification | Walking imagery evaluation in brain computer interfaces via a multi-view multi-level deep polynomial network | MMDPN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Brain functionality classification | Deep Learning Based on Event-Related EEG Differentiates Children with ADHD from Healthy Controls | CNN, RNN | J. Clin. Med. | 2019 |
| Brain functionality classification | Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders | EL-SDAE | Comput. Biol. Med. | 2019 |
| Brain functionality classification | Assaying neural activity of children during video game play in public spaces: A deep learning approach | CNN | J. Neural Eng. | 2019 |
| Brain functionality classification | Learning joint space–time–frequency features for EEG decoding on small labeled data | CNN | Neural Networks | 2019 |
| Brain functionality classification | Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods | CNN | Sensors | 2019 |
| Brain functionality classification | Validating deep neural networks for online decoding of motor imagery movements from EEG signals | LSTM, CNN, RCNN | Sensors | 2019 |
| Brain functionality classification | Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment | CNN, TCN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Brain functionality classification | On the Vulnerability of CNN Classifiers in EEG-Based BCIs | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Brain functionality classification | Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture | SNN | Sci. Rep. | 2018 |
| Brain functionality classification | Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning | SAE, DCNN | IEEE Trans. Biomed. Eng. | 2018 |
| Brain functionality classification | Deep learning enabled automatic abnormal EEG identification | 1D-CNN, RNN | IEEE EMBC | 2018 |
| Brain functionality classification | A hybrid network for ERP detection and analysis based on restricted Boltzmann machine | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2018 |
| Brain functionality classification | Deep learning for detection of focal epileptiform discharges from scalp EEG recordings | CNN, RNN | Clin. Neurophysiol. | 2018 |
| Brain functionality classification | Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment | RNN, 3D-CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2018 |
| Brain functionality classification | Learning temporal information for brain-computer interface using convolutional neural networks | RNN, 3D-CNN | IEEE Trans. Neural Networks Learn. Syst. | 2018 |
| Brain functionality classification | Prediction of bispectral index during target-controlled infusion of propofol and remifentanil | LSTM | Anesthesiology | 2018 |
| Brain functionality classification | DeepMI: Deep Learning for Multiclass Motor Imagery Classification | CNN | IEEE EMBC | 2018 |
| Brain disease classification | Aberrant epileptic seizure identification: A computer vision perspective | CNN, LSTM | Seizure | 2019 |
| Brain disease classification | A deep learning framework for automatic diagnosis of unipolar depression | 1D-CNN, 1D-CNN, LSTM | Int. J. Med. Inform. | 2019 |
| Brain disease classification | Automatic analysis of EEGs using big data and hybrid deep learning architectures | HMM, SDAE | Front. Hum. Neurosci. | 2019 |
| Brain disease classification | Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning | DCssCDBM | Neural Networks | 2019 |
| Brain disease classification | Early prediction of epileptic seizures using a long-term recurrent convolutional network | CNN, LSTM | J. Neurosci. Methods | 2019 |
| Brain disease classification | EEG Classification of MotorI magery Using a Novel Deep Learning Framework | Sensors | 2019 | |
| Brain disease classification | Deepmulti-viewfeature learning for EEG-based epileptic seizure detection | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Brain disease classification | EEG-based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features | CNN, VAE | Hum. Brain Mapp. | 2019 |
| Brain disease classification | Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals | CNN | Brain Sci. | 2019 |
| Brain disease classification | Classification of epileptic EEG recordings using signal transforms and convolutional neural networks | CNN | Comput. Biol. Med. | 2019 |
| Brain disease classification | Efficient Epileptic Seizure Prediction based on Deep Learning | CNN, LSTM | IEEETrans.Biomed. Circ. Syst. | 2019 |
| Brain disease classification | Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals | LSTM | Clin. Neurophysiol. | 2019 |
| Brain disease classification | AMulti-DomainConnectomeConvolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns | CNN | IEEE J. Biomed. Health Inf. | 2019 |
| Brain disease classification | fNIR Simproves seizure detection In multi modal EEG-fNIRS recordings | LSTM | J. Biomed. Opt. | 2019 |
| Brain disease classification | Dual deep neural network-based classifiers to detect experimental seizures | LSTM | TheKorean Can. J. Physiol. Pharmacol. | 2019 |
| Brain disease classification | Automated tracking of level of consciousness and delirium in critical illness using deep learning. | CNN, LSTM | NPJ Digital Med. | 2019 |
| Brain disease classification | Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease | CNN, RNN, DNN | Int. J. Environ. Res. Publ. Health | 2018 |
| Brain disease classification | Automated EEG-based screening of depression using deep convolutional neural network | CNN | Comput. Methods Programs Biomed. | 2018 |
| Brain disease classification | DeepIED:Anepilepticdischargedetector for EEG-fMRI based on deep learning | CNN | NeuroImage Clin. | 2018 |
| Brain disease classification | Toward simproved design and evaluation of epileptic seizure predictors | CNN | IEEE Trans. Biomed. Eng. | 2018 |
| Brain disease classification | Epileptic seizure prediction using big data and deep learning: Toward a mobile system | CNN | EBioMedicine | 2018 |
| Brain disease classification | ALongShort-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals | LSTM | Comput. Biol. Med. | 2018 |
| Brain disease classification | Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery | CNN | Epilepsia | 2018 |
| Brain disease classification | Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images | CNN | Comput. Biol. Med. | 2018 |
| Brain disease classification | Automatic seizure detection using three-dimensional CNN based on multi-channel EEG | CNN | BMC Med. Inf. Decis. Making | 2018 |
| Brain disease classification | A Multi-View Deep Learning Framework for EEG Seizure Detection | CNN | IEEE J. Biomed. Health Inf. | 2018 |
| Brain disease classification | Confusion State Induction and EEG-based Detection in Learning | CNN | IEEE EMBC | 2018 |
| Brain disease classification | Detection of Early Stage Alzheimer’s Disease using EEG Relative Power with Deep Neural Network | CNN | IEEE EMBC | 2018 |
| Brain disease classification | Deep classification of epileptic signals | CNN, AE | IEEE EMBC | 2018 |
| Emotion classification | EEG emotion recognition model based on the LIBSVM classifier | LIBSVM | Measurement | 2020 |
| Emotion classification | SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition | 3D Dense Network | ACM MM | 2020 |
| Emotion classification | EEG-based emotion recognition via channel-wise attention and self attention | CNN, RNN | IEEE Trans. Affect. Comput. | 2020 |
| Emotion classification | A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition | TANN | arXiv | 2020 |
| Emotion classification | Emotion Recognition under Sleep Deprivation Using a Multimodal Residual LSTM Network | LSTM | IJCNN | 2020 |
| Emotion classification | A novel bi-hemispheric discrepancy model for eeg emotion recognition | BiHDM | IEEE TCDS | 2020 |
| Emotion classification | Emotion Classification Using EEG Brain Signals and the Broad Learning System | BLS | IEEE Trans. SMC: Systems | 2020 |
| Emotion classification | A Multi-Column CNN Model for Emotion Recognition from EEG Signals | CNN | Sensors | 2019 |
| Emotion classification | SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG | LSTM | Front Neurorobotics | 2019 |
| Emotion classification | Learning CNN features from DE features for EEG-based emotion recognition | CNN | Pattern Anal Appl | 2019 |
| Emotion classification | Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models | CNN | arXiv | 2019 |
| Emotion classification | Using the center loss function to improve deep learning performance for EEG signal classification. | CNN, LSTM | ICACI | 2019 |
| Emotion classification | Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks | CNN | IEEE Access | 2019 |
| Emotion classification | Subject-Independent Emotion Recognition During Music Listening Based on EEG Using Deep Convolutional Neural Networks | CNN | CSPA | 2019 |
| Emotion classification | EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations | PLV | IEEE Trans. Biomed. Eng. | 2019 |
| Emotion classification | EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks and Broad Learning System | DGCNN, BLS | BIBM | 2019 |
| Emotion classification | Emotion Recognition from Multiband EEG Signals Using CapsNet | CapsNet | Sensors | 2019 |
| Emotion classification | Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques | DNN | Sensors | 2019 |
| Emotion classification | Recognition of emotions using multichannel EEG data and DBN-GC-based ensemble deep learning framework | DBN | Comput. Intell. Neurosci. | 2018 |
| Emotion classification | A Bi-hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition | LSTM | IEEE Trans. Affect. Comput. | 2018 |
| Emotion classification | Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition | CNN | ICONIP | 2018 |
| Emotion classification | EEG Emotion Recognition Based on Granger Causality and CapsNet Neural Network. | CNN | CCIS | 2018 |
| Emotion classification | EEG Emotion Classification Based On Baseline Strategy | CNN | CCIS | 2018 |
| Emotion classification | Cross-corpus EEG-based emotion recognition | CNN | MLSP | 2018 |
| Emotion classification | Comparison of Facial Emotion Recognition Based on Image Visual Features and EEG Features | GRU | ICCSIP | 2018 |
| Emotion classification | On the influence of affect in EEG-based subject identification | AdaBOOST | IEEE Trans. Affect. Comput. | 2018 |
| Emotion classification | Empirical Evidence Relating EEG Signal Duration to Emotion Classification Performance | HOC's | IEEE Trans. Affect. Comput. | 2018 |
| Emotion classification | A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG | CFCNN, RQA | Chaos | 2018 |
| Emotion classification | Data Encoding Visualization Based Cognitive Emotion Recognition with AC-GAN Applied for Denoising. | GAN | ICCI* CC | 2018 |
| Emotion classification | EmotioNet: A 3-D Convolutional Neural Network for EEG-based Emotion Recognition | CNN | IJCNN | 2018 |
| Emotion classification | Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network | CNN, RNN | IJCNN | 2018 |
| Emotion classification | A mutual information based adaptive windowing of informative EEG for emotion recognition | Window Selection | IEEE Trans. Affect. Comput. | 2018 |
| Emotion classification | Convolutional neural network approach for EEG-based emotion recognition using brain connectivity and its spatial information | CNN | ICASSP | 2018 |
| Emotion classification | Eeg emotion recognition using dynamical graph convolutional neural networks | DGCNN | IEEE Trans. Affect. Comput. | 2018 |
| Emotion classification | Improvement on Speech Emotion Recognition Based on Deep Convolutional Neural Networks | CNN | ICCAI | 2018 |
| Emotion classification | Data augmentation for eeg-based emotion recognition with deep convolutional neural networks | SVM / LeNet /ResNet | ICMM | 2018 |
| Emotion classification | Spatial–Temporal Recurrent Neural Network for Emotion Recognition | RNN | IEEE transactions on cybernetics | 2018 |
| Emotion classification | EEG-Based Emotion Recognition using 3D Convolutional Neural Networks | CNN | Int. J. Adv. Comput. Sci. Appl | 2018 |
| Emotion classification | Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system | 2D-CNN | Sensors | 2018 |
| Emotion classification | Spatial–temporal recurrent neural network for emotion recognition | RNN | IEEE Trans. Cybern. | 2018 |
| Emotion classification | Emotionmeter: A multimodal framework for recognizing human emotions | RBM | IEEE Trans. Cybern. | 2018 |
| Emotion classification | A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface | CNN | IEEE EMBC | 2018 |
| Emotion classification | Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management | LSTM | Healthcare Inf. res. | 2018 |
| Emotion classification | Hierarchical convolutional neural networks for EEG-based emotion recognition | CNN | Cognit Comput | 2017 |
| Emotion classification | Emotions Recognition Using EEG Signals: A Survey | Survey | IEEE Trans. Affect. Comput. | 2017 |
| Emotion classification | Identifying Stable Patterns over Time for Emotion Recognition from EEG | GRELM | IEEE Trans. Affect. Comput. | 2017 |
| Emotion classification | Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset | DNN, CNN | AAAI | 2017 |
| Emotion classification | Gaussian process dynamical models for multimodal affect recognition | SVM | IEEE EMBC | 2016 |
| Emotion classification | Multimodal Emotion Recognition Using Multimodal Deep Learning | DAE | arXiv | 2016 |
| Emotion classification | Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection | RNN, CCRF | IEEE Trans. Affect. Comput. | 2015 |
| Emotion classification | Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks | DBN | IEEE transactions on cybernetics | 2015 |
| Emotion classification | Eeg-based emotion recognition using deep learning network with principal component based covariate shift adaptation | CNN | IEEE CAMSAP | 2015 |
| Emotion classification | A Novel Semi-Supervised Deep Learning Framework for Affective State Recognition on EEG Signals | ST, AL | IEEE ICBB | 2014 |
| Emotion classification | Eeg-based emotion classification using deep belief networks | DBN | ICIC | 2014 |
| Gender classification | Predicting sex from brain rhythms with deep learning | CNN | Sci. Rep. | 2018 |
| Words classification | Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks | DN-AE-NTM | Neural Networks | 2019 |
| Age classification | Machine learning for MEG during speech tasks | CNN | Sci. Rep. | 2019 |
| EEG decoding and visualization | Deep learning with convolutional neural networks for eeg decoding and visualization | AE | IEEE CSPA | 2017 |
| Epileptogenicity localization | Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fmri and eeg big data | CNN | Hum. Brain Mapp. | 2017 |
| Decoding excited movements | Designing and understanding convolutional networks for decoding executed movements from eeg | CNN | IEEE ICAC | 2017 |
| Butcome prediction for patients with a postanoxic coma after cardiac arrest | Deep learning for outcome prediction of postanoxic coma | CNN | BNTC | 2017 |
| Discriminate brain activity | Deep learning human mind for automated visual classification | CNN | EMBEC & NBC | 2017 |
| BCI | Truenorth-enabled real-time classification of eeg data for brain-computer interfacing | CNN | CVPR | 2017 |
| BCI | Decoding eeg and lfp signals using deep learning: heading truenorth | CNN | Signal Process. Image Commun. | 2016 |
| Seizure detection | Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals | CNN | IEEE EMBC | 2017 |
| Tracking of neural dynamics | Brain activity recognition with a wearable fnirs using neural networks | RBM | IEEE Trans. Neural Syst. Rehabil. Eng. | 2017 |
| Prediction of driver’s cognitive performance | Eeg-based prediction of driver’s cognitive performance by deep convolutional neural network | CNN, DNN | IEEE ICMA | 2017 |
| Response representation | Deep learning eeg response representation for brain computer interface | CNN | ACM CF | 2016 |
| Prediction of driver’s cognitive performance | Prediction of driver’s drowsy and alert states from eeg signals with deep learning | CNN | CCC | 2015 |
| Feature extraction | Convolutional deep belief networks for feature extraction of eeg signal | RBM | IEEE BIBE | 2014 |
| Detecting target images | A deep learning method for classification of images rsvp events with eeg data | CNN | IJCNN | 2014 |
| Epileptic seizure prediction | Comparing svm and convolutional networks for epileptic seizure prediction from intracranial eeg | DBN | GlobalSIP | 2013 |
| Task/Application | Title | Model | Publication | Year |
|---|---|---|---|---|
| Disease Detection | Ml–resnet: a novel network to detect and locate myocardial infarction using 12 leads ecg | CNN | Comput. Methods Progr. Biomed. | 2020 |
| Disease Detection | Multi-branch fusion network for myocardial infarction screening from 12-lead ecg images | CNN | Comput. Methods Progr. Biomed. | 2020 |
| Disease Detection | Transfer learning in ecg classification from human to horse using a novel parallel neural network architecture | CNN | Sci. Rep. | 2020 |
| Disease Detection | Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram | CRNN | J. Electrocardiol. | 2020 |
| Disease Detection | Mfb-cbrnn: a hybrid network for mi detection using 12-lead ecgs | CRNN | IEEE J. Biomed. Health Inf. | 2020 |
| Disease Detection | Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks | CRNN | PloS One | 2020 |
| Disease Detection | Detection of strict left bundle branch block by neural network and a method to test detection consistency | NN with Expert Features | Physiological Measurement | 2020 |
| Disease Detection | I-vector based patient adaptation of deep neural networks for automatic heartbeat classification | FC & Others | IEEE J. Biomed. Health Inf. | 2020 |
| Disease Detection | Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram | CNN | Nat. Med. | 2019 |
| Disease Detection | Classification of myocardial infarction with multi-lead ecg signals and deep cnn | CNN | Pattern Recogn. Lett. | 2019 |
| Disease Detection | Deep learning to automatically interpret images of the electrocardiogram: do we need the raw samples? | CNN | J. Electrocardiol. | 2019 |
| Disease Detection | Accurate detection of atrial fibrillation from 12-lead ecg using deep neural network | CNN | Comput. Biol. Med. | 2019 |
| Disease Detection | Atrial fibrillation detection using an improved multi-scale decomposition enhanced residual convolutional neural network | CNN | IEEE Access | 2019 |
| Disease Detection | A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals | CNN | IEEE Access | 2019 |
| Disease Detection | Spectro-temporal feature based multi-channel convolutional neural network for ecg beat classification | CNN | IEEE EMBC | 2019 |
| Disease Detection | Real-time detection of acute cognitive stress using a convolutional neural network from electrocardiographic signal | CNN | IEEE Access | 2019 |
| Disease Detection | Automatic cardiac arrhythmia classification using combination of deep residual network and bidirectional lstm | CNN | IEEE Access | 2019 |
| Disease Detection | Combining deep neural networks and engineered features for cardiac arrhythmia detection from ecg recordings | CNN | Physiol. Meas. | 2019 |
| Disease Detection | Ecg arrhythmia classification using stft-based spectrogram and convolutional neural network | CNN | IEEE Access | 2019 |
| Disease Detection | Atrial fibrillation prediction with residual network using sensitivity & orthogonality constraints | CNN | IEEE JBHI | 2019 |
| Disease Detection | A novel multi-module neural network system for imbalanced heartbeats classification | CNN | Expert Syst. Appl. X | 2019 |
| Disease Detection | An automatic system for real-time identifying atrial fibrillation by using a lightweight convolutional neural network | CNN | IEEE access | 2019 |
| Disease Detection | Automated heartbeat classification exploiting convolutional neural network with channel-wise attention | CNN | IEEE access | 2019 |
| Disease Detection | Automated heartbeat classification using 3-d inputs based on convolutional neural network with multi-fields of view | CNN | IEEE access | 2019 |
| Disease Detection | Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network | CNN | Physiol. Meas. | 2019 |
| Disease Detection | Classification of atrial fibrillation recurrence based on a convolution neural network with svm architecture | CNN | IEEE Access | 2019 |
| Disease Detection | Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ecg analysis | CNN | CIRC-ARRHYTHMIA ELEC | 2019 |
| Disease Detection | Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ecg recordings | CNN | J. Electrocardiol. | 2019 |
| Disease Detection | A robust deep convolutional neural network with batch-weighted loss for heartbeat classification | CNN | Expert Syst. Appl. | 2019 |
| Disease Detection | Developing convolutional neural networks for deep learning of ventricular action potentials to predict risk for ventricular arrhythmias | CNN | Circulation | 2019 |
| Disease Detection | Detecting and Interpreting Myocardial Infarction Using Fully Convolutional Neural Networks | CNN | Physiological measurement | 2019 |
| Disease Detection | Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-occurrence Matrix Enhanced Convolutional Neural Network | CNN | IEEE Access | 2019 |
| Disease Detection | Cardiovascular disease diagnosis using cross-domain transfer learning | CNN | IEEE EMBC | 2019 |
| Disease Detection | Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease | CNN | CinC | 2019 |
| Disease Detection | Early and Remote Detection of Possible Heartbeat Problems with Convolutional Neural Networks and Multipart Interactive Training | CNN | IEEE Access | 2019 |
| Disease Detection | A deep learning method to detect atrial fibrillation based on continuous wavelet transform | CNN | IEEE EMBC | 2019 |
| Disease Detection | A novel wearable electrocardiogram classification system using convolutional neural networks and active learning | CNN | IEEE Access | 2019 |
| Disease Detection | Pvc recognition for wearable ecgs using modified frequency slice wavelet transform and convolutional neural network | CNN | CinC | 2019 |
| Disease Detection | Beatgan: anomalous rhythm detection using adversarially generated time series | CNN | IJCAI | 2019 |
| Disease Detection | Pgans: personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification | RNN | AAAI | 2019 |
| Disease Detection | Interpretability analysis of heartbeat classification based on heartbeat activity's global sequence features and bilstm-attention neural network | RNN | IEEE Access | 2019 |
| Disease Detection | Automatic Classification of Cad Ecg Signals with Sdae and Bidirectional Long Short-Term Term Network | RNN | IEEE Access | 2019 |
| Disease Detection | A parallel gru recurrent network model and its application to multi-channel time-varying signal classification | RNN | IEEE Access | 2019 |
| Disease Detection | Localization of myocardial infarction with multi-lead bidirectional gated recurrent unit neural network | RNN | IEEE Access | 2019 |
| Disease Detection | Dense convolutional networks with focal loss and image generation for electrocardiogram classification | CRNN | IEEE Access | 2019 |
| Disease Detection | A deep learning approach for real-time detection of atrial fibrillation | CRNN | Expert Syst. Appl. | 2019 |
| Disease Detection | Deepheart: semi-supervised sequence learning for cardiovascular risk prediction | CRNN | AAAI | 2019 |
| Disease Detection | Mina: multilevel knowledge-guided attention for modeling electrocardiography signals | CRNN | IJCAI | 2019 |
| Disease Detection | Detection of first-degree atrioventricular block on variable-length electrocardiogram via a multimodal deep learning method | CRNN | CinC | 2019 |
| Disease Detection | Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram | CRNN | IEEE Access | 2019 |
| Disease Detection | A lstm and cnn based assemble neural network framework for arrhythmias classification | CRNN | ICASSP | 2019 |
| Disease Detection | Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach | CRNN | ICASSP | 2019 |
| Disease Detection | Deep ensemble detection of congestive heart failure using short-term rr intervals | CRNN | IEEE Access | 2019 |
| Disease Detection | Ecg arrhythmias detection using auxiliary classifier generative adversarial network and residual network | CRNN | IEEE Access | 2019 |
| Disease Detection | Pay attention and watch temporal correlation: a novel 1-d convolutional neural network for ecg record classification | CRNN | Physiol. Meas. | 2019 |
| Disease Detection | Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram | CRNN | IEEE Access | 2019 |
| Disease Detection | K-margin-based residual-convolution-recurrent neural network for atrial fibrillation detection | CRNN | IJCAI | 2019 |
| Disease Detection | Automated detection and localization of myocardial infarction with staked sparse autoencoder and treebagger | AE | IEEE Access | 2019 |
| Disease Detection | A probabilistic process neural network and its application in ecg classification | FC & Others | IEEE Access | 2019 |
| Disease Detection | Detection of atrial fibrillation and other abnormal rhythms from ecg using a multi-layer classifier architecture | FC & Others | Physiol. Meas. | 2019 |
| Disease Detection | Electrocardiographic screening for atrial fibrillation while in sinus rhythm using deep learning | CNN | Circulation | 2018 |
| Disease Detection | Deep learning to detect atrial fibrillation from short noisy ecg segments measured with wireless sensors | CNN | Circulation | 2018 |
| Disease Detection | Ecg classification using three-level fusion of different feature descriptors | CNN | Expert Syst. Appl. | 2018 |
| Disease Detection | A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length | CNN | Physiol. Meas. | 2018 |
| Disease Detection | Patient-specific ecg classification by deeper cnn from generic to dedicated | CNN | Neurocomputing | 2018 |
| Disease Detection | Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure | CNN | IEEE Access | 2018 |
| Disease Detection | Real-time multilead convolutional neural network for myocardial infarction detection | CNN | IEEE JBHI | 2018 |
| Disease Detection | Preprocessing method for performance enhancement in cnn-based stemi detection from 12-lead ecg | CNN | IEEE Access | 2018 |
| Disease Detection | Analyzing single-lead short ecg recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation | CNN | Physiol. Meas. | 2018 |
| Disease Detection | Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ecg recordings | CNN | CinC | 2018 |
| Disease Detection | Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning | CNN | KDD | 2018 |
| Disease Detection | A convolutional neural network for ecg annotation as the basis for classification of cardiac rhythms | CNN | Physiol. Meas. | 2018 |
| Disease Detection | Monitoring significant st changes through deep learning | CNN | J. Electrocardiol. | 2018 |
| Disease Detection | Ecg signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network | CNN | Physiol. Meas. | 2018 |
| Disease Detection | Automated ecg classification using dual heartbeat coupling based on convolutional neural network | CNN | IEEE Access | 2018 |
| Disease Detection | Af detection by exploiting the spectral and temporal characteristics of ecg signals with the lstm model | RNN | CinC | 2018 |
| Disease Detection | A generative modeling approach to limited channel ecg classification | RNN | IEEE EMBC | 2018 |
| Disease Detection | Abductive reasoning as a basis to reproduce expert criteria in ecg atrial fibrillation identification | RNN | Physiol. Meas. | 2018 |
| Disease Detection | Premature ventricular contraction detection from ambulatory ecg using recurrent neural networks | RNN | IEEE EMBC | 2018 |
| Disease Detection | Automated detection of atrial fibrillation using long short-term memory network with rr interval signals | CRNN | Comput. Biol. Med. | 2018 |
| Disease Detection | Generalization studies of neural network models for cardiac disease detection using limited channel ecg | CRNN | CinC | 2018 |
| Disease Detection | Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks | CRNN | KDD | 2018 |
| Disease Detection | Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection | CRNN | Physiol. Meas. | 2018 |
| Disease Detection | Bidirectional recurrent neural network and convolutional neural network (bircnn) for ecg beat classification | CRNN | IEEE EMBC | 2018 |
| Disease Detection | Classification of atrial fibrillation using stacked auto encoders neural networks | AE | CinC | 2018 |
| Disease Detection | An automatic cardiac arrhythmia classification system with wearable electrocardiogram | AE | IEEE Access | 2018 |
| Disease Detection | Parallel use of a convolutional neural network and bagged tree ensemble for the classification of holter ecg | NN with Expert Features | Physiol. Meas. | 2018 |
| Disease Detection | A 10-rr-interval-based rhythm classifier using a deep neural network | FC & Others | Circulation | 2018 |
| Disease Detection | Artificial intelligence detects pediatric heart murmurs with cardiologist-level accuracy | FC & Others | Circulation | 2018 |
| Disease Detection | Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ecg | CNN | CinC | 2017 |
| Disease Detection | Atrial fibrillation detection using feature based algorithm and deep convolutional neural network | CNN | CinC | 2017 |
| Disease Detection | Encase: an ensemble classifier for ecg classification using expert features and deep neural networks | CNN | CinC | 2017 |
| Disease Detection | Personalized monitoring and advance warning system for cardiac arrhythmias | CNN | Sci. Rep. | 2017 |
| Disease Detection | Atrial fibrillation detection and ecg classification based on convolutional recurrent neural network | CNN | CinC | 2017 |
| Disease Detection | Atrial fibrillation detection using stationary wavelet transform and deep learning | CNN | CinC | 2017 |
| Disease Detection | Robust ecg signal classification for detection of atrial fibrillation using a novel neural network | CNN | CinC | 2017 |
| Disease Detection | Atrial fibrillation classification using qrs complex features and lstm | RNN | CinC | 2017 |
| Disease Detection | Beat by beat: classifying cardiac arrhythmias with recurrent neural networks | RNN | CinC | 2017 |
| Disease Detection | Cardiovascular risk stratification using off-the-shelf wearables and a multi-task deep learning algorithm | CRNN | Circulation | 2017 |
| Disease Detection | Cardiac arrhythmia detection from ecg combining convolutional and long short-term memory networks | CRNN | CinC | 2017 |
| Disease Detection | Convolutional recurrent neural networks for electrocardiogram classification | CRNN | CinC | 2017 |
| Disease Detection | Ecg monitoring system integrated with ir-uwb radar based on cnn | CNN | IEEE Access | 2016 |
| Disease Detection | Convolutional neural networks for patient-specific ecg classification | CNN | IEEE EMBC | 2015 |
| Heart beat classification | Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study | CNN | J. Electrocardiol. | 2019 |
| Heart beat classification | ECG anomaly class identification using LSTM and error profile modeling | LSTM+SVM, LSTM+MLR, LSTM+MLP | Comput. Biol. Med. | 2019 |
| Heart beat classification | A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation | CNN | J. Electrocardiol. | 2019 |
| Heart beat classification | Detection of congestive heart failure based on LSTM-based deep network via short-term RR intervals. | LSTM | Sensors | 2019 |
| Heart beat classification | A novel ECG signal compression method using spindle convolutional auto-encoder | AE | Comput. Methods Programs Biomed. | 2019 |
| Heart beat classification | A new deep learning model for assisted diagnosis on electrocardiogram | CNN+BRNN | Math. Biosci. Eng. MBE | 2019 |
| Heart beat classification | LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices | LSTM | IEEE J. Biomed. Health Inf. | 2019 |
| Heart beat classification | Electrocardiogram Beat-Classification Based on a ResNet Network | CNN | Stud. Health technol. Inf. | 2019 |
| Heart beat classification | Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal | CNN | IEEE EMBC | 2019 |
| Heart beat classification | Inter-Patient ECG Classification with Symbolic Representations and Multi-Perspective Convolutional Neural Networks | CNN | IEEE J. Biomed. Health Inf. | 2019 |
| Heart beat classification | Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction | CNN | J. Cardiovascu Electrophysiol. | 2019 |
| Heart beat classification | A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet | DL-CCANet, TL-CCANet | Sensors | 2019 |
| Heart beat classification | Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model | CNN | Healthcare Inf. Res. | 2019 |
| Heart beat classification | Intelligent deep models based on scalograms of electrocardiogram signals for biometrics | ResNet | Sensors | 2019 |
| Heart beat classification | Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia | 1D-CNN+ LSTM | PLoS ONE | 2019 |
| Heart beat classification | Automated Agatston score computation in non-ECG gated CT scans using deep learning | CNN | In Proceedings of the Medical Imaging | 2018 |
| Heart beat classification | Detecting atrial fibrillation by deep convolutional neural networks | STFT+CNN, SWT+CNN | Comput. Biol. Med. | 2018 |
| Heart beat classification | Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals | LSTM+CNN | Comput. Biol. Med. | 2018 |
| Heart beat classification | Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study | CNN | PLoS ONE | 2018 |
| Heart beat classification | Towards end-to-end ECG classification with raw signal extraction and deep neural networks | CNN+RBM | IEEE J. Biomed. Health Inf. | 2018 |
| Heart beat classification | A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification | DBLSTM-WS | Comput. Biol. Med. | 2018 |
| Heart beat classification | Classification of Premature Ventricular Contraction using Error Back-Propagation | EBR | KSII Trans. Internet Inf. Syst. | 2018 |
| Heart beat classification | A novel application of deep learning for single-lead ECG classification | RBM+DBM | Comput. Biol. Med. | 2018 |
| Heart beat classification | Noise Detection in Electrocardiography Signal for Robust Heart Rate Variability Analysis: A Deep Learning Approach | CNN | IEEE EMBC | 2018 |
| Heart beat classification | Region Aggregation Network: Improving Convolutional Neural Network for ECG Characteristic Detection | CNN+RA | IEEE EMBC | 2018 |
| Heart beat classification | Patient-specific deep architectural model for ecg classification | DNN | J. Healthcare Eng. | 2017 |
| Heart beat classification | A deep convolutional neural network model to classify heartbeats | CNN | Comput. Biol. Med. | 2017 |
| Heart beat classification | Real-time patient-specific ecg classification by 1-d convolutional neural networks | CNN | IEEE Trans. Biomed. Eng. | 2016 |
| Arrhythmia classification | Robust greedy deep dictionary learning for ecg arrhythmia classification | Robust deep dictionary learning | IJCNN | 2017 |
| Arrhythmia classification | Automated detection of arrhythmias using different intervals of tachycardia ecg segments with convolutional neural network | CNN | Inf. Sci. | 2017 |
| Coronary artery disease detection | Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network | CNN | Future Gener. Comput. Syst. | 2017 |
| Coronary artery disease detection | Time series classification using multi-channels deep convolutional neural networks | CNN | WAIM | 2014 |
| Myocardial infarction Detection | Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals | CNN | Inf. Sci. | 2017 |
| Features for Screening Paroxysmal Atrial Fibrillatio | Deep convolutional neural networks and learning ecg features for screening paroxysmal atrial fibrillation patients | CNN | IEEE Trans. Syst. Man Cybern.Syst. | 2017 |
| Fetal-ECG signal reconstruction | A deep learning approach to fetal-ecg signal reconstruction | Stacked Denoising Autoencoder | IEEE NCC | 2016 |
| Identification of ventricular arrhythmias | Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ecg signals: a comparative study | CNN | Inf. Sci. | 2017 |
| Monitoring and detecting atrial fibrillation | A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology | CNN | IEEE EMBS BHI | 2017 |
| Heart disease classification | A new approach for arrhythmia classification using deep coded features and LSTM networks | AE+LSTM | Comput. Methods Programs Biomed. | 2019 |
| Heart disease classification | Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal | CNN | J. Korean Med. Sci. | 2019 |
| Heart disease classification | Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types | AE+U-net | Comput. Biol. Med. | 2019 |
| Heart disease classification | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network | CNN | Nat. Med. | 2019 |
| Heart disease classification | Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals | 1D-CNN | Comput. Biol. Med. | 2019 |
| Heart disease classification | Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network | Faster RCNN | Sensors | 2019 |
| Heart disease classification | Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: A deep-learning approach | CNN | Biomed. Eng. Online | 2019 |
| Heart disease classification | Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress | CNN+LSTM | Sensors | 2019 |
| Heart disease classification | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats | CNN+LSTM | Comput. Biol. Med. | 2018 |
| Heart disease classification | LiteNet: Lightweight neural network for detecting arrhythmias at resource-constrained mobile devices | CNN | Sensors | 2018 |
| Heart disease classification | Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks | MLP, CNN | Bioengineering | 2018 |
| Heart disease classification | Arrhythmia detection using deep convolutional neural network with long duration ECG signals | 1D-CNN | Comput. Biol. Med. | 2018 |
| Heart disease classification | A deep learning approach to examine ischemic ST changes in ambulatory ECG recordings | CNN | AMIA Summits Transl. Sci. Proc. | 2018 |
| Heart disease classification | Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory | LSTM | IEEE EMBC | 2018 |
| Heart disease classification | Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators | CNN | Sci. Rep. | 2018 |
| Sleep stage classification | Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram | CNN | Comput. Methods Progr. Biomed. | 2019 |
| Sleep stage classification | A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals | RNN | IEEE Access | 2019 |
| Sleep stage classification | A RR interval based automated apnea detection approach using residual network | CNN | Comput. Methods Programs Biomed. | 2019 |
| Sleep stage classification | A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement | CNN | Sensors | 2019 |
| Sleep stage classification | Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network | CNN | PeerJ | 2019 |
| Sleep stage classification | Sleep-wake classification via quantifying heart rate variability by convolutional neural network | CNN | Physiol. Meas. | 2018 |
| Sleep stage classification | Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram | CNN | Physiol. Meas. | 2018 |
| Sleep stage classification | A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ecg signal | AE | Neurocomputing | 2018 |
| Sleep stage classification | Obstructive sleep apnoea detection using convolutional neural network based deep learning framework | CNN | Biomed. Eng. lett. | 2018 |
| Age and gender prediction | Age and sex estimation using artificial intelligence from standard 12-lead ECGs | CNN | Circ. Arrhythmia Electrophysiol. | 2019 |
| ECG identification | Ecg based identification by deep learning | DNN | CCBR | 2017 |
| Emotion classification | Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals. | RNN+CNN | TELEMEDICINE e-HEALTH | 2018 |
| Signal quality classification | Classifying measured electrocardiogram signal quality using deep belief networks | RBM | IEEE I2MTC | 2017 |
| Sleep apnea detection | Recurrent neural network based classification of ecg signal features for obstruction of sleep apnea detection | RNN | IEEE CSE and IEEE EUC | 2017 |
| Annotation or Localization | Impact of ecg dataset diversity on generalization of cnn model for detecting qrs complex | CNN | IEEE Access | 2019 |
| Annotation or Localization | An electrocardiogram delineator via deep segmentation network | CNN | IEEE EMBC | 2019 |
| Annotation or Localization | U-net architecture for the automatic detection and delineation of the electrocardiogram | CNN | CinC | 2019 |
| Annotation or Localization | Qrs detection method based on fully convolutional networks for capacitive electrocardiogram | CNN | Expert Syst. Appl. | 2019 |
| Annotation or Localization | Automated and interpretable patient ecg profiles for disease detection, tracking, and discovery | CNN | Circ Cardiovasc Qual Outcomes | 2019 |
| Annotation or Localization | Sequential factorized autoencoder for localizing the origin of ventricular activation from 12-lead electrocardiograms | RNN | IEEE Trans. Biomed. Eng. | 2019 |
| Annotation or Localization | An ensemble of deep recurrent neural networks for p-wave detection in electrocardiogram | RNN | ICASSP | 2019 |
| Annotation or Localization | Inter-patient cnn-lstm for qrs complex detection in noisy ecg signals | CRNN | IEEE Access | 2019 |
| Annotation or Localization | Deep learning based qrs multilead delineator in electrocardiogram signals | CNN | CinC | 2018 |
| Annotation or Localization | Qrs detection and measurement method of ecg paper based on convolutional neural networks | CNN | IEEE EMBC | 2018 |
| Annotation or Localization | A deep learning approach for fetal qrs complex detection | RNN | Physiol. Meas. | 2018 |
| Annotation or Localization | Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ecg | CNN | IEEE Trans. Biomed. Eng. | 2017 |
| Annotation or Localization | Annotating ecg signals with deep neural networks | RNN | Circulation | 2017 |
| Annotation or Localization | Automatic coordinate prediction of the exit of ventricular tachycardia from 12-lead electrocardiogram | AE | CinC | 2017 |
| Denoising | End-to-end trained cnn encoder-decoder network for fetal ecg signal denoising | CNN+BRNN | Physiological Measurement | 2020 |
| Denoising | Noise reduction in ecg signals using fully convolutional denoising autoencoders | CNN | IEEE Access | 2019 |
| Denoising | Deep convolutional encoder-decoder framework for fetal ecg signal denoising | CNN | CinC | 2019 |
| Denoising | Noise rejection for wearable ecgs using modified frequency slice wavelet transform and convolutional neural networks | CNN | IEEE Access | 2019 |
| Denoising | Adversarial de-noising of electrocardiogram | GAN | Neurocomputing | 2019 |
| Denoising | Elimination of power line interference from ecg signals using recurrent neural networks | RNN | IEEE EMBC | 2017 |
| Denoising | A stacked contractive denoising auto-encoder for ecg signal denoising | AE | Physiol. Meas. | 2016 |
| Others | Generalization of convolutional neural networks for ecg classification using generative adversarial networks | CNN | IEEE Access | 2020 |
| Others | Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ecg | CRNN | Sci. Rep. | 2020 |
| Others | Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder | AE | arXiv | 2020 |
| Others | Ecg generation with sequence generative adversarial nets optimized by policy gradient | GAN | IEEE Access | 2020 |
| Others | Electrocardiogram generation with a bidirectional lstm-cnn generative adversarial network | GAN | Sci. Rep. | 2020 |
| Others | A deep learning model to predict outcome after thoracoscopic surgery for atrial fibrillation using single beat electrocardiographic samples | CNN | Circulation | 2019 |
| Others | A comparison of patient history-and ekg-based cardiac risk scores | CNN | AMIA Summits Transl. Sci. Proc. | 2019 |
| Others | Biosignal generation and latent variable analysis with recurrent generative adversarial networks | RNN | IEEE Access | 2019 |
| Others | Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients | CRNN | PloS One | 2019 |
| Others | Automatic driver stress level classification using multimodal deep learning | CRNN | Expert Syst. Appl. | 2019 |
| Others | Synthesis of electrocardiogram v lead signals from limb lead measurement using r peak aligned generative adversarial network | GAN | IEEE J. Biomed. Health Inf. | 2019 |
| Others | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure | FC & Others | PloS One | 2019 |
| Others | Using deep convolutional neural network for emotion detection on a physiological signals dataset (amigos) | CNN | IEEE Access | 2018 |
| Others | Deep learning for pulse detection in out-of-hospital cardiac arrest using the ecg | CRNN | CinC | 2018 |
| Others | Raim: recurrent attentive and intensive model of multimodal patient monitoring data | CRNN | KDD | 2018 |
| Others | Recognition of emotions using multimodal physiological signals and an ensemble deep learning model | AE | Comput. Methods Progr. Biomed. | 2017 |
| Others | Ecg data compression using a neural network model based on multi-objective optimization | FC & Others | PloS One | 2017 |
| Others | Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals | AE | IEEE Trans. Biomed. Eng. | 2016 |
| Task/Application | Title | Model | Publication | Year |
|---|---|---|---|---|
| Hand gesture classification | CNN-Based Detection and Classification of Grasps Relevant for Worker Support Scenarios Using sEMG Signals of Forearm Muscles | CNN | IEEE SMC | 2019 |
| Hand gesture classification | Deep Reinforcement Learning Apply in Electromyography Data Classification | CNN | IEEE CBS | 2019 |
| Hand gesture classification | A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface | CNN | Pattern Recogn. Lett. | 2019 |
| Hand gesture classification | Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net | DBN | IEEE J. Biomed. Health Inf. | 2019 |
| Hand gesture classification | Surface Electromyography-based Gesture Recognition by Multi-view Deep Learning | CNN | IEEE Trans. Biomed. Eng. | 2019 |
| Hand gesture classification | Deep learning for electromyographic hand gesture signal classification using transfer learning | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Hand gesture classification | EMG-Based Hand-Gesture Classification Using a Generative Flow Model | GFM | Sensors | 2019 |
| Hand gesture classification | Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth | CNN | Sci. Rep. | 2019 |
| Hand gesture classification | Neural muscle activation detection: A deep learning approach using surface electromyography | RNN | J. Biomech. | 2019 |
| Hand gesture classification | Deep learning for musculoskeletal force prediction | CNN | Ann. Biomed. Eng. | 2019 |
| Hand gesture classification | Deep Learning Movement Intent Decoders Trained with Dataset Aggregation for Prosthetic Limb Control | CNN/LSTM | IEEE Trans. Biomed. Eng. | 2019 |
| Hand gesture classification | Deep learning for waveform identification of resting needle electromyography signals | CNN | Clin. Neurophysiol. | 2019 |
| Hand gesture classification | A novel approach for classification of hand movements using surface EMG signals | AE | IEEE ISSPIT | 2018 |
| Hand gesture classification | An adaptive convolutional neural network framework for multi-user myoelectric interfaces | CNN | IEEE ACPR | 2018 |
| Hand gesture classification | A Hybrid Cnn-SVM Classifier for Hand Gesture Recognition with Surface Emg Signals | CNN | IEEE ICMLC | 2018 |
| Hand gesture classification | Auto-encoder based deep learning for surface electromyography signal processing | AE | Adv. Sci., Technol. Eng. Syst. | 2018 |
| Hand gesture classification | Classification of multichannel surface-electromyography signals based on convolutional neural networks | CNN | J. Ind. Inform. Integration | 2018 |
| Hand gesture classification | Compact deep neural networks for computationally efficient gesture classification from electromyography signals | CNN | IEEE ROBIO | 2018 |
| Hand gesture classification | Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation | RNN | IEEE ICARCV | 2018 |
| Hand gesture classification | Deep learning in EMG-based gesture recognition | CNN | PhyCS | 2018 |
| Hand gesture classification | Estimating the Direction of Force Applied to the Grasped Object Using the Surface EMG | CNN | LNCS | 2018 |
| Hand gesture classification | Feasibility study of advanced neural networks applied to sEMG-based force estimation | CNN-RNN | Sensors (Switzerland) | 2018 |
| Hand gesture classification | Hand gesture recognition based on deep learning method | CNN | IEEE DSC | 2018 |
| Hand gesture classification | Hand gestures recognition using machine learning for control of multiple quadrotors | CNN | IEEE SAS | 2018 |
| Hand gesture classification | Movement and Gesture Recognition Using Deep Learning and Wearable-sensor Technology | CNN-RNN | ACM ICPS | 2018 |
| Hand gesture classification | Multiday emg-based classification of hand motions with deep learning techniques | CNN | Sensors | 2018 |
| Hand gesture classification | Pca and deep learning based myoelectric grasping control of a prosthetic hand | AE | Biomed. Eng. Online | 2018 |
| Hand gesture classification | Performance of Combined Surface and Intramuscular EMG for Classification of Hand Movements | AE | IEEE EMBS | 2018 |
| Hand gesture classification | Real-time, simultaneous myoelectric control using a convolutional neural network | CNN | PLoS ONE | 2018 |
| Hand gesture classification | Recurrent neural network models for myoelectricbased control of a prosthetic hand | RNN | IEEE ICSTCC | 2018 |
| Hand gesture classification | semg-based gesture recognition with convolution neural networks | CNN | Sustainability | 2018 |
| Hand gesture classification | Sensor Fusion for Myoelectric Control Based on Deep Learning With Recurrent Convolutional Neural Networks | CNN-RNN | Artif. Organs | 2018 |
| Hand gesture classification | Stacked sparse autoencoders for EMG-based classification of hand motions: A comparative multi day analyses between surface and intramuscular EMG | AE | Applied Sciences (Switzerland) | 2018 |
| Hand gesture classification | Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron | RNN | IEEE EMBS | 2018 |
| Hand gesture classification | The deep neural network based classification of fingers pattern using electromyography | AE | IEEE IMCEC | 2018 |
| Hand gesture classification | Touchsense: Classifying and measuring the force of finger touches with an electromyography armband | CNN | ACM ICPS | 2018 |
| Hand gesture classification | Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks | CNN | Procedia Manufacturing | 2018 |
| Hand gesture classification | Personal authentication and hand motion recognition based on wrist EMG analysis by a convolutional neural network | CNN | IEEE IOTAIS | 2018 |
| Hand gesture classification | A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition | CNN+RNN | PLoS ONE | 2018 |
| Hand gesture classification | EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks | CNN+RNN | Artif. Organs | 2018 |
| Hand gesture classification | Finger motion estimation based on frequency conversion of EMG signals and image recognition using convolutional neural network | CNN | IEEE EMBC | 2017 |
| Hand gesture classification | Multimodal deep learning network based hand ADLs tasks classification for prosthetics control | CNN-AE | IEEE ICPIC | 2017 |
| Hand gesture classification | Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network | CNN | Front. Neurosci. | 2017 |
| Hand gesture classification | Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation | CNN | Sensors (Switzerland) | 2017 |
| Muscle activity recognition | Transfer learning for sEMG hand gestures recognition using convolutional neural networks | CNN | IEEE ICSMC | 2017 |
| Muscle activity recognition | EMG pattern classification by split and merge deep belief network | DBN | Symmetry | 2016 |
| Muscle activity recognition | Movement intention decoding based on deep learning for multiuser myoelectric interfaces | CNN | IWW-BCI | 2016 |
| Muscle activity recognition | Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands | CNN | Front. Neurorobot. | 2016 |
| Muscle activity recognition | Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience | DBN | J. Central South University | 2015 |
| Gesture recognition | Gesture recognition by instantaneous surface emg images | CNN | Sci. Rep. | 2016 |
| Robotic arm guidance | A convolutional neural network for robotic arm guidance using semg based frequency-features | CNN | IEEE IROS | 2016 |
| Speech and emotion classification | Personal authentication by lips EMG using dry electrode and CNN | CNN | IEEE IOTAIS | 2019 |
| Speech and emotion classification | Human emotion recognition using deep belief network architecture | DBN | Inform. Fusion | 2019 |
| Speech and emotion classification | Deep belief network based affect recognition from physiological signals | DBN | UPCON | 2018 |
| Speech and emotion classification | Subvocal speech recognition via close-talk microphone and surface electromyogram using deep learning | CNN | FedCSIS | 2017 |
| Speech and emotion classification | Deep neural network frontend for continuous emg-based speech recognition | DNN | INTERSPEECH | 2016 |
| Speech and emotion classification | Direct conversion from facial myoelectric signals to speech using Deep Neural Networks | DNN | IEEE IJCNN | 2015 |
| Speech and emotion classification | Pattern learning with deep neural networks in emg-based speech recognition | DNN | IEEE EMBC | 2014 |
| Sleep stage classification | Deep learning method for sleep stage classification | CNN | ICONIP | 2018 |
| Sleep stage classification | A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series | CNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2018 |
| Sleep stage classification | Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks | CNN | IEEE EMBS | 2018 |
| Sleep stage classification | A compact deep learning network for temporal sleep stage classification | CNN-RNN | IEEE LSC | 2018 |
| Sleep stage classification | Visualising convolutional neural network decisions in automated sleep scoring | CNN | CEUR-WS | 2018 |
| Sleep stage classification | Combining deep belief networks and bidirectional long short-term memory: Case study: Sleep stage classification | DBN | EECSI | 2017 |
| Amyotrophic Lateral Sclerosis detection | Deepemgnet: An application for efficient discrimination of ALS and normal EMG signals | CNN | Adv. Intell. Syst. Comput. | 2018 |
| EMG Signal Estimation from rat | Convolutional networks outperform linear decoders in predicting EMG from spinal cord signals | CNN | Front. Neurosci. | 2018 |
| EMG Signal Quality Rating | Development of a deep neural network for automated electromyographic pattern classification | CNN | J. Exp. Biol. | 2019 |
| EMG Signal Synthesis | Biosignals learning and synthesis using deep neural networks | RNN | BioMedical Eng. Online | 2017 |
| Lower-Limb Joint Angle Estimation | Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks | DBN | Biomed. Signal Process. Control | 2018 |
| Muscle Fatigue Detection | Measurement of upper limb muscle fatigue using deep belief networks | DBN | J. Mech. Med. Biol. | 2016 |
| Spinal Cord Signals Amyotrophic Lateral Sclerosis detection | Deep neural network assisted diagnosis of time-frequency transformed electromyograms | DNN | Multimedia Tools Appl. | 2018 |
| Task/Application | Title | Model | Publication | Year |
|---|---|---|---|---|
| Sleep stage classification | Interactive sleep stage labelling tool for diagnosing sleep disorder using deep learning | RNN | IEEE EMBC | 2018 |
| Help paralyzed persons | Nine states HCI using electrooculogram and neural networks | NN | IJET | 2017 |
| Help paralyzed persons | EOG signal classification using neural network for human computer interaction | NN | IJCTA | 2016 |
| Help paralyzed persons | Classification of eye movements using electrooculography and neural networks | NN | IJHCI | 2014 |
| Help paralyzed persons | Idendifying eye movements using neural networks for human computer interaction | NN | IJCA | 2014 |
| Help paralyzed persons | A novel efficient human computer interface using an electrooculogram | SFCM | IJRET | 2014 |
| Help paralyzed persons | An EOG-based sleep monitoring system and its application on on-line sleep-stage sensitive light control | LDA | PhyCS | 2014 |
| Help paralyzed persons | Recognition of eye movementelectrooculogram signalsusing dynamic neuralnetworks | NN | KJCS | 2013 |
| Help paralyzed persons | Natural eye movement & its application for paralyzed patients | KNN | IJETT | 2013 |
| Help paralyzed persons | Development strategy of eye movement controlled rehabilitation aid using electrooculogram | NN | IJSER | 2012 |
| Help paralyzed persons | HMM based continuous EOG recognition for eye-input speech interface | HMM | ISCA | 2012 |
| Help paralyzed persons | Electro occulogram based interactive robotic arm interface for partially paralytic patients | MCDFA | IJITEE | 2012 |
| Help paralyzed persons | EOG-based visual navigation interface development | MCDFA | ESA | 2012 |
| Help paralyzed persons | Research on electrooculography classification based on multiple features | SVM | IJDCTA | 2012 |
| Help paralyzed persons | Online voluntary eye blink detection using electrooculogram | SVM | Int Symp Nonlinear Theor Appl | 2012 |
| Help paralyzed persons | A novel human-machine interface based on recognition of multi-chNNel facial bioelectric signals | SFCM | APESM | 2011 |
| Help paralyzed persons | Considerations on strategies to improve EOG signal analysis | LDA | ACM IJALR | 2011 |
| Help paralyzed persons | On the use of electrooculogram for efficient human computer interfaces | KNN | CIN | 2010 |
| Help paralyzed persons | Human electrooculography interface | KNN | Lisbon Technical University | 2010 |
| Help paralyzed persons | Eye movement analysis for activity recognition using electrooculographgy | SVM | IEEE TPAMI | 2010 |
| Help paralyzed persons | A feasibility study of an eye-writing system based on electro-oculography | SFCM | JMBE | 2008 |
| Help paralyzed persons | Electrooculogram classification of microcontroller based interface system | MCDFA | SIEDS | 2007 |
| Help paralyzed persons | Eye computer interface (ECI) and human machine interface applications to help handicapped persons | NN | TOJEEE | 2006 |
| Help paralyzed persons | Classification of electro-oculogram signals using artificial neural network | NN | ESA | 2006 |
| Help paralyzed persons | A biosignal-based human interface controlling a power-wheelchair for people with motor disabilities | HMM | ETRIJ | 2006 |
| Help paralyzed persons | System for assisted mobility using eye movements based on electrooculography | NN | IEEE Transact NSRE | 2002 |
| Help paralyzed persons | EOG guidance of a wheelchair using neural network | NN | ICPR | 2000 |
| Task/Application | Title | Model | Publication | Year |
|---|---|---|---|---|
| Sleep stage classification | An Automatic Sleep Staging Model Combining Feature Learning and Sequence Learning | CNN,LSTM | ICACI | 2020 |
| Sleep stage classification | An Automatic Sleep Staging Method Using a Multi-head and Sequence Network | CNN,LSTM | ICBIBE | 2020 |
| Sleep stage classification | GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification | ST-GCN | IJCAI | 2020 |
| Sleep stage classification | SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging | RNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2019 |
| Sleep stage classification | A deep learning model for automated sleep stages classification using PSG signals | CNN | Int. J. Environ. Res. Public Health | 2019 |
| Sleep stage classification | An ultra-low-power dual-mode automatic sleep staging processor using neural-network-based decision tree | WPD,DT | IEEE Trans. Circuits Syst. I, Reg. Papers | 2019 |
| Sleep stage classification | Deep learning for automated feature discovery and classification of sleep stages | CNN | IEEE/ACM Trans. Comput. Biol. Bioinf. | 2019 |
| Sleep stage classification | Multivariate sleep stage classification using hybrid self-attentive deep learning networks | Self-attention | BIBM | 2018 |
| Sleep stage classification | Expert-level sleep scoring with deep neural networks | CNN,LSTM | J Am Med Inform Assn | 2018 |
| Sleep stage classification | Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device | BiLSTM | Comput. Biol. Med. | 2018 |
| Sleep stage classification | Joint classification and prediction CNN framework for automatic sleep stage classification | CNN | IEEE Trans. Biomed. Eng. | 2018 |
| Sleep stage classification | Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms | CNN | IEEE EMBC | 2018 |
| Sleep stage classification | Multichannel sleep stage classification and transfer learning using convolutional neural networks | CNN | IEEE EMBC | 2018 |
| Sleep stage classification | An end-to-end framework for real-time automatic sleep stage classification | CNN | Sleep | 2018 |
| Sleep stage classification | A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series | CNN,LSTM | IEEE Trans. Neural Syst. Rehabil. Eng. | 2018 |
| Sleep stage classification | Personalizing deep learning models for automatic sleep staging | CNN | arXiv | 2018 |
| Sleep stage classification | Visualising convolutional neural network decisions in automatic sleep scoring | CNN | CEUR Workshop Proceedings | 2018 |
| Sleep stage classification | Deep Convolutional Neural Networks for feature-less automatic classification of Independent Components in multi-channel electrophysiological brain recordings | CNN | IEEE Trans. Biomed. Eng. | 2018 |
| Sleep stage classification | Mixed neural network approach for temporal sleep stage classification | MLP,RNN | IEEE Trans. Neural Syst. Rehabil. Eng. | 2017 |
| Sleep stage classification | Electrooculogram based sleep stage classification using deep belief network | DBN | IJCNN | 2015 |
| Sleep stage classification | Sleep stage classification using unsupervised feature learning | DBN | Adv. Artif. Neural Syst. | 2012 |
| Sleep stage classification | Investigation of an automatic sleep stage classification by means of multiscorer hypnogram | LDS | Methods Inf Med | 2010 |
| Sleep stage classification | Classification of human sleep stages based on EEG processing using hidden Markov models | HMM | Biomed. Eng. | 2007 |
| Disease Detection | Simultaneous human health monitoring and time-frequency sparse representation using eeg and ecg signals | CNN | IEEE Access | 2019 |
| Speech and emotion classification | Emotion Analysis Using Audio/Video, EMG and EEG: A Dataset and Comparison Study | DBN | IEEE WACV | 2018 |
| Driving fatigue detection | Detecting driving fatigue with multimodal deep learning | AE | IEEE/EMBS NER | 2017 |
| Momentary mental workload classification | Pattern recognition of momentary mental workload based on multi-channel electrophysiological data and ensemble convolutional neural networks | CNN | Front. Neurosci. | 2017 |
| Multimodal Data Compression | Multimodal deep learning approach for Joint EEG-EMG Data compression and classification | AE | IEEE WCNC | 2017 |
| Drowsiness detection | Eog-based drowsiness detection using convolutional neural networks | CNN | IJCNN | 2014 |
| Emotion classification | Emotion recognition using multimodal residual LSTM network | LSTM | ACM ICM | 2019 |
| Emotion classification | Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing | CNN, LSTM | IEEE Trans. Affective Comput. | 2019 |
| Emotion classification | EmotionMeter: A Multimodal Framework for Recognizing Human Emotions | RBM | IEEE transactions on cybernetics | 2018 |
| Emotion classification | Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS) | CNN | IEEE Access | 2018 |
| Emotion classification | Deep belief network based affect recognition from physiological signals | DBN | UPCON | 2017 |
| Emotion classification | EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes | SLFN | IEEE Trans. Cogn. Devel. Syst. | 2017 |
Yuanlai He, Ziyu Jia, and Xiyang Cai collaborated to organize and summarize the above papers.