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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 57739 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Shamsollahi, Mohammad Bagher
- Abstract:
- The electroencephalogram (EEG) microstates, as reflections of the brain's quasi-stable neural activity, play a crucial role in analyzing brain dynamics and understanding mental processes. These microstates represent specific and transient states in the brain's neuroelectric dynamics and can provide valuable insights into the brain's structure and function. Investigating the temporal dynamics of these microstates, their relationship with mental processes, and changes caused by disorders such as depression helps improve our understanding of the brain's complex mechanisms. Major depressive disorder is one of the most common diseases globally, associated with widespread effects on quality of life and brain and physiological changes, including neuroelectric and cardiovascular abnormalities. In this study, four microstates labels {A,B,C,D} were extracted for two groups: healthy and depressed, in two conditions (eyes-opened and eyes-closed) at rest. After extracting the static and dynamic features of these microstates, their relationship with the simultaneous occurrence of the R peak in the electrocardiogram (ECG) signal was also examined. The findings revealed that the microstates in the depressed group, especially with eyes-closed, had lower separability compared to the healthy group. Meanwhile, microstates B and D in the healthy group with eyes-opened showed the highest separability, associated with activation of brain resources in visual and cognitive functions. The static features of the microstates showed significant changes between the healthy and depressed groups. In the depressed group, the number of occurrences of microstate B increased, and its duration decreased, while there was an increased transition to this microstate from others. These differences indicate abnormalities in visual processing in depressed individuals. The reduced presence of microstate C in the depressed group may indicate a decreased reliance on the default mode network in depression. Analysis of microstate sequences showed lower disorder in the distribution of microstates with eyes-opened, especially in the healthy group, compared to eyes-closed. Long-term persistence was observed in all microstates, indicating continuity and long-term patterns in brain activity. Additionally, results showed that the mean duration of microstates in the eyes-closed condition was shorter than in the eyes-opened condition, aligning with previous studies that suggest an increase in the dominant frequency of brain oscillations with eyes-closed. The Lempel-Ziv algorithm showed higher pattern complexity in the depressed group, with complexity decreasing significantly with eyes-opened. These findings were consistent with the results of the transition matrix symmetry analysis. Moreover, the Hurst exponent, which was applied only to stationary sequences, showed greater long-term dependence in the eyes-closed condition, which refers explicitly to reduced environmental interference. Although both groups exhibited random distributions of microstates with R-peak occurrences in ECG signals in the eyes-closed condition, in the eyes-opened condition, microstate A had the highest and microstate D the lowest occurrence in both groups. Statistical tests and classification achieved 66% accuracy (eyes-closed) and 81% (eyes-opened), emphasizing the importance of temporal microstate features, especially for depression analysis in the eyes-opened condition
- Keywords:
- Depressive Disorder ; Electroencephalogram Signals Classification ; Microstate Dynamics ; R-Peak ; Electroencephalography ; Signal Classification
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