Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders
Pinar Ozel1, Ali Karaca2, Ali Olamat3
1Department of Biomedical Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey.
International Journal of Neural Systems
|September 9, 2020
Summary
This study introduces novel electroencephalography (EEG) signal analysis methods for diagnosing obsessive-compulsive disorder (OCD). MEMD-based synchronization analysis offers deeper insights into OCD
Area of Science:
- Neuroscience
- Signal Processing
- Computational Psychiatry
Background:
- Obsessive-compulsive disorder (OCD) is a neuropsychiatric condition characterized by intrusive thoughts and repetitive behaviors.
- Advanced signal processing techniques are increasingly used for OCD diagnosis.
- Existing synchronization methods may not fully capture the complexity of electroencephalography (EEG) signals in OCD.
Purpose of the Study:
- To propose and evaluate novel synchronization measures for analyzing EEG signals in individuals with OCD.
- To investigate the utility of Multivariate Empirical Mode Decomposition (MEMD) for characterizing EEG complexity in OCD.
- To demonstrate the superiority of MEMD-based nonlinear synchronization analysis over traditional methods for OCD diagnosis.
Main Methods:
- Utilized Multivariate Empirical Mode Decomposition (MEMD) to decompose EEG signals into intrinsic mode functions.
- Developed four novel synchronization measures: intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity.
- Applied statistical tests (sample t-test and F-test) to evaluate the significance of the proposed methodology.
Main Results:
- MEMD-based synchronization analysis provided more detailed insights into EEG signals compared to standalone synchronization methods.
- The nonlinear synchronization approach demonstrated greater consistency in results, accounting for OCD heterogeneity.
- Statistical evaluations confirmed the significance of the novel methodology for OCD analysis.
Conclusions:
- The proposed MEMD-based intrinsic synchronization analysis is a valuable tool for understanding EEG signal complexity in OCD.
- This data-driven, nonlinear approach offers a more nuanced and consistent method for diagnosing OCD.
- The findings highlight the potential of advanced signal processing for improving neuropsychiatric disorder diagnostics.
