A Multi-channel EEG Biomarker Weighted Spectral Clustering Model for MDD Prediction

The article designs an approach for the detection of MDD using spectral clustering. The raw EEG is pre-processed, and then three quantitative biomarkers: band power (beta, delta, and theta) along with three signal-extracted signals: Detrended Fluctuation Analysis (DFA), Higuchi’s Fractal Dimension (HFD), and Lempel-Ziv Complexity (LZC) are extracted. The data is scrutinized in both inter and intra hemispheric regions. A weighted graph is constructed based on the data that is clustered to obtain the condition of the subject. The results showcase both the efficiency and efficacy of the designed approach. The accuracy achieved for the left hemisphere is 98% and CEP of 2.5%, while 97% accuracy and 3.3% CEP for the right hemisphere. In this case, channel-wise maximum accuracy has been achieved for Fp1 and F8