Automatic characterization of affective states in individuals with mood disorders based on the analysis of brain-heart interactions

Abstract

In 2019, approximately 9% of the Canadian population experienced mood disorders such as depression or bipolar disorder. The diagnosis and treatment of these conditions are often hindered by factors like social stigma, limited clinical resources, and the lack of reliable objective markers. To address this gap, this thesis explores the potential of integrating multiple bio-signals that capture both brain and heart activity to provide a more comprehensive understanding of mood disorders, particularly depression, during sleep. The primary aim of this research is to investigate the pathophysiological mechanisms underlying mood disorders by analyzing the interaction between sleep electroencephalogram (EEG) and electrocardiogram (ECG) signals. This study introduces a coherence metric as a potential interrelated biomarker for depression, linking brain and heart activity. A secondary analysis of polysomnography data from 46 individuals with depression and 40 healthy controls was conducted, revealing significant differences in brain-heart coherence of depression and healthy groups across sleep stages and EEG channels, particularly in the 0-8 Hz frequency bands. In parallel, this thesis develops SleepDepNet, a deep learning model designed to automate the detection of depression by leveraging EEG and ECG biomarkers such as relative power ratio, heart rate, and the introduced coherence metric. SleepDepNet combines convolutional neural networks with long short-term memory networks to analyze the temporal and spectral characteristics of these signals. The model demonstrated a high accuracy of 98.33% in classifying depression, validating the efficacy of using brain-heart interactions as diagnostic tools. These findings suggest that integrating EEG and ECG along with deep learning algorithms offers a promising approach for the objective identification of mood disorders and lays the groundwork for future research into their automated detection and prediction.

Date	18 Jun 2025
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Mohamad Forouzanfar (Supervisor), Rébecca Robillard (Co-supervisor) & Jean-Marc Lina (Co-supervisor)