Depression Identification Using EEG Signals via a Hybrid of LSTM and
Spiking Neural Networks
Abstract
Depression severity can be classified into distinct phases based on the
Beck depression inventory (BDI) test scores, a subjective questionnaire.
However, quantitative assessment of depression may be attained through
the examination and categorization of electroencephalography (EEG)
signals. Spiking neural networks (SNNs), as the third generation of
neural networks, incorporate biologically realistic algorithms, making
them ideal for mimicking internal brain activities while processing EEG
signals. This study introduces a novel framework that for the first
time, combines an SNN architecture and a long short-term memory (LSTM)
structure to model the brainâ\euro™s underlying structures during
different stages of depression and effectively classify individual
depression levels using raw EEG signals. By employing a brain-inspired
SNN model, our research provides fresh perspectives and advances
knowledge of the neurological mechanisms underlying different levels of
depression. The methodology employed in this study includes the
utilization of the synaptic time dependent plasticity (STDP) learning
rule within a 3-dimensional braintemplate structured SNN model.
Furthermore, it encompasses the tasks of classifying and predicting
individual outcomes, visually representing the structural alterations in
the brain linked to the anticipated outcomes, and offering
interpretations of the findings. Notably, our method achieves
exceptional accuracy in classification, with average rates of 98% and
96% for eyes-closed and eyes-open states, respectively. These results
significantly outperform state-of-the-art deep learning methods.