Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Sparse Coding Classification and Analytic EEG Signal Representation for Obsessive Compulsive Disorder Detection

Document Type : Full Research Paper

Authors

Farzaneh Manzari 1
Peyvand Ghaderyan 2

1 M.Sc. Student, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

2 Associate Professor, Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

https://doi.org/10.22041/ijbme.2022.542095.1733
Abstract
Obsessive-Compulsive Disorder (OCD) is the fourth most common mental disorder and the tenth cause of disability worldwide. This disorder can lead to cognitive impariments in attention, memory, thinking, auditory processing of words and visual cognition. Previous studies have demonstrated that OCD is associated with changes in connectivity between different lobes of the brain. Hence, the quantification of symmetry and connectivity between different brain regions has attracted great attention. This study has provided a new efficient approach based on analytic representation of EEG signals and statistical features to quantify the difference of intrinsic components of brain activity between brain lobes. For this purpose, phase spectra and amplitude envelopes of the analytic EEG signals have been extracted and analyzed. Furthermore, Non-Negative Least Square sparse classification method has been used for discriminating between healthy control group and OCD patients. The detection capability of the proposed method has been studied in 19 healthy subjects and 11 patients, performing simple flanker tasks. The obtained results have demonstrated the effectiveness of the combined amplitude and phase information in OCD detection with high average accuracy rate of 93.78 %. In comparison between different regions, the inter-hemispheric features and those extracted from the frontal lobe and frontal-parietal network have shown more efficiency in diagnosing the OCD. This study has also highlighted more importance of amplitude information in the OCD detection.

Keywords

Empirical Mode Decomposition (EMD)
Statistical Features
Non-Negative Least Square Sparse (NNLS)
Amplitude Envelope
Phase Spectra
Flanker Task

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 15, Issue 4
Winter 2022
Pages 313-328

  • Receive Date 03 November 2021
  • Revise Date 27 March 2022
  • Accept Date 03 April 2022

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