/ADHD

Primary LanguageMATLAB

Wavelet-based Approach for Diagnosing Attention Deficit Hyperactivity Disorder (ADHD)

In this project, a noval method is proposed based the self-similar property of pupillary dynamics for early detection of ADHD.

  1. Self-similarity is characterized in the wavelet domain by computing wavelet spectra.
  2. A distance variance based approach is proposed to compute wavelet spectra as it is more robust to noise and outliers.
  3. A set of localized discriminatory features are constructed and selected via a rolling window method to build classifiers.
  4. ADHD detection performance is evaluated by using three classifiers, Logistic Regression, Support Vector Machine, and k-nearrest neighbor.
  5. To compare performance of the proposed method, a feature engineering technique that uses features constructed in the original pupil diameter data domain is considered. More details about the feature engineering method can be found in Das et al., A Robust MAchine Learning based Framework for the Automated Detection of ADHD Using the Pupillometric Biomarkers and Time Series Analysis, Scientific Reports, 2021(https://www.nature.com/articles/s41598-021-95673-5).

Below a brief overview of the pupil data analysis procedure is presented.

Dataset

  1. The dataset (pupil_dataset.mat) used in this project is available at https://figshare.com/articles/dataset/ADHD_Pupil_Size_Dataset/7218725/3.

  2. The dataset consists of pupil diameter data collected from 50 children (28 cases and 22 control) during a visuospatial working memory task. The dataset contains three pupil diameter data groups, namely ADHD-diagnosed children with and without medication and normal children.

  3. More information about the dataset is given in the study, Rojas-Líbano et al, A pupil size, eye-tracking and neuropsychological dataset from ADHD children during a cognitive task, Scientific Data, 2019.

  4. This project considered pupil diameter of the AHDHD-diagnosed children without medication and normal children.

Matlab Codes

MatlabFunctions folder contains matlab functions used in the project. You can run the project as follows.

  1. ReadAndSaveData.m computes feature matrixes for self-similarity and feature engineering methods.

  2. Demo_1.m is for computing the ROC curve and distribution of Hurst exponent for cases and controls.

  3. Demo_2.m evaluating classification performance of the proposed self-similarity and feature engineering methods.