Machine Learning Based Framework for Depression Diagnosis Using EEG and ECG Signals

Diagnosing depression manually requires patience, attention to detail, and significant expertise.The current approach leverages electroencephalogram (EEG) and electrocardiogram (ECG) signals to identify and diagnose depression.The proposed work aims to develop a machine learning-based system that uses EEG and ECG data for the effective detection of depression.Feature extraction and selection strategies, separate approaches, are employed in the design of classification methodologies.Key segments of the ECG signal P, QRS, and ST are extracted as functional features.From EEG signals, the most significant features include alpha band power, entropy, standard deviation, and Hjorth activity (HA).EEG data are classified using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models, while ECG signals are analyzed using Long Short-Term Memory (LSTM) Autoencoders and Recurrent Neural Network (RNN) architectures.The performance of these classifiers in terms of accuracy, sensitivity, selectivity, and specificity is enhanced when two-dimensional sequence inputs are utilized in RNNs and LSTM Autoencoders.The current approach achieves a 93% accuracy rate for ECG signal classification, while CNN outperforms SVM in EEG signal classification with an accuracy of 97.69%.