The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning - Maddury, Sucheer (Frontiers in Signal Processing, 2024)
Early detection of Huntington's disease (HD) can substantially improve patient quality of life.Current HD diagnosis methods include complex biomarkers such as clinical and imaging factors, however these methods have high time and resource demand. Quantitative biomedical signaling has potential for exposing abnormalities in HD patients. In this project, we attempted to explore biomedical signalling for HD diagnosis in high detail. we used a dataset collected at a clinic with 27 HD-positive patients, 36 controls, and 6 unknowns with EEG, ECG, and fNIRS. We first preprocessed the data and then presented a comprehensive feature extraction procedure for statistical, Hijorth, slope, wavelet, and power spectral features. We then applied several shallow machine learning techniques to classify HD-positives from controls. We found the highest accuracy was achieved by the Extremely Randomized Trees algorithm, with ROC AUC of 0.963 and accuracy of 91.353%. The results provide performance improvement over competing methodologies and also show promise for biomedical signals to prognose HD early.