main_self.py
: Use the ecg data collected by ourselves to find the best dimension for classificationmain_mit.py
: The MIT dataset was used to find the best combination of frequency-domain features suitable for classificationShowResult.py
: Show some resultstest.py
: Generate the data we wantutils.py
: Some functions used inmain_self.py
、main_mit.py
...text.txt
: Experimental results looking for the best combination and the best dimension
rrinterval_seg.m
: MIT_BIH dataset segment, based on label_time saved in ATRTIMEQ1.m
: Our ecg data preprocess, denoise is not included, which is done via Matlab 1-D Wavelet toolbox.rdata.m
: MIT_BIH dataset preprocess, including how to read the file of 'hea, art...', from Machine_Learning_ECG-master.
Data_H5: FFT data、 DWT data and so on.
data_self_dwt.h5
data_self_fft.h5
data_self_src.h5
......
model_LinearSVC: LinearSVC models trained with FFT and DWT data to find the best dimension.
model_lsvc_fft+dwt_16.pkl
model_lsvc_fft+dwt_32.pkl
model_lsvc_fft+dwt_64.pkl
......
model_pca: PCA models trained with FFT and DWT data which is used as a dimension reduction.
model_pca_fft+dwt_16.pkl
model_pca_fft+dwt_16.pkl
model_pca_fft+dwt_16.pkl
......
out: Frequency domain characteristics of ecg signals.
fear_1_frequency.mat
fear_2_frequency.mat
joy_10_frequency.mat
......