A Diagnostic Strategy via Multiresolution Synchrosqueezing Transform on Obsessive Compulsive Disorder

ÖZEL P., Olamat A., AKAN A.

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, vol.31, no.12, 2021 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 12
  • Publication Date: 2021
  • Doi Number: 10.1142/s0129065721500441
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Keywords: Electroencephalography, obsessive-compulsive disorder, multi-variate synchrosqueezing transform, TIME-FREQUENCY ANALYSIS, HILBERT SPECTRUM, QUANTITATIVE EEG, CONNECTIVITY, COMPLEXITY, SYNCHRONIZATION, CLASSIFICATION, NETWORKS, GRAPH, QEEG


This research presents a new method for detecting obsessive-compulsive disorder (OCD) based on time-frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time-frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.