EEG-derived brainwave patterns for depression diagnosis via hybrid machine learning and deep learning frameworks

In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040. For early diagnosis, a patient-friendly, cost-effective approach based on readily observable and objective indicators is essential. The objective of this research is to develop machine learning and deep learning techniques that utilize electroencephalogram (EEG) signals to diagnose depression. Different statistical features were extracted from the EEG signals and fed into the models. Three classifiers were constructed: 1D Convolutional Neural Network (1DCNN), Support Vector Machine (SVM), and Logistic Regression (LR). The methods were tested on a dataset comprising EEG signals from 34 patients with Major Depressive Disorder (MDD) and 30 healthy subjects. The signals were collected under three distinct conditions: TASK, when the subject was performing a task; Eye Close (EC), when the subject's eyes were closed; and Eye Open (EO), when the subject's eyes were open. All three classifiers were applied to each of the three types of signals, resulting in nine (3 × 3) experiments. The results showed that TASK signals yielded the highest accuracies of 88.4%, 89.3%, and 90.21% for LR, SVM, and 1DCNN, respectively, compared to EC and EO signals. Additionally, the proposed methods outperformed some state-of-the-art approaches. These findings highlight the potential of EEG-based approaches for the clinical diagnosis of depression and provide promising avenues for further research. Additionally, the proposed methodology demonstrated statistically significant improvements in classification accuracy, with p-values < 0.05, ensuring robustness and reliability.