Differential Information Extraction of Electroencephalogram Signals for Obsessive-Compulsive Disorder Detection

Subject Areas : Intelligent Multimedia Processing and Communication Systems (IMPCS)

Farzaneh Manzari ¹ (Master student , Faculty of biomedical Engineering, Sahand University of Technology, Tabriz)
Peyvand Ghaderyan ² (biomedical engineering faculty, Sahand University of Technology, Tabriz, Iran)

Received: 2022-03-06 Accepted : 2022-10-12 Published : 2021-03-21

Keywords: Support vector machine (SVM), Empirical Mode Decomposition (EMD), Differential statistical features, Obsessive-Compulsive Disorder (OCD),

Abstract :

Introduction: Obsessive-Compulsive Disorder (OCD) is a chronic mental and social disease that is prevalent in about 2 to 3% of the human population leading to cognitive impairments and affected quality of patient's life. Therefore, a reliable and timely diagnosis can help psychiatrists in better treating or controlling this disease.Method: Previous studies have demonstrated interdependence impairments between different brain regions in patients with OCD. Hence, this study has provided a new approach based on the decomposition of signals into intrinsic components and extraction of differential transient changes in amplitude envelope and phase spectra of the EEG signal recorded during Flanker tasks. The proposed algorithm has been evaluated using 19 healthy subjects and 11 patients by the Support Vector Machine (SVM) classifier.Result: The obtained results have confirmed the capability of the proposed method in diagnosing the disease with high accuracy of 93.89% using amplitude differential information of the electroencephalogram signal.Conclusion: In comparison between different regions, the statistical features extracted from the frontal lobe, the frontal-parietal network, and the inter-hemispheric features have offered better detection ability.

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Differential Information Extraction of Electroencephalogram Signals for Obsessive-Compulsive Disorder Detection