{"title":"The utility of electroencephalographic measures in obsession compulsion disorder","authors":"","doi":"10.1016/j.bspc.2024.107113","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Obsessive-compulsive disorder (OCD) is a potentially serious mental disorder that affects 1–2% of the world population. The OCD patients experience uncontrollable, and recurring thoughts (obsessions), and may feel a need to repeat behaviors (compulsions). In EEG studies, many different features have been investigated regarding their OCD diagnosis capability. However, there is no OCD study evaluating different EEG features with the same conditions and data.</div></div><div><h3>Methods</h3><div>To address the problem, we employed six popular resting-state EEG features, including absolute and relative power, Phase locking value (PLV), Weighted phase lag index (WPLI), approximate entropy, and Higuchi’s fractal dimension, to find out which feature can better discriminate OCDs from healthy controls (CON) under the same conditions and data. All the generated features were normalized using mean and standard deviation of values, calculated from 233 Iranian healthy people. After that, the most informative EEG features, discriminating 39 OCD individuals from age, handedness, and gender-matched, 39 CON were selected and entered into the classification process. In addition, an independent EEG dataset including 23 OCDs and 23 CONs was also used to investigate the consistency of the results.</div></div><div><h3>Results</h3><div>As expected, most of the significant differences were observed at the high frequency bands in Beta I-IV, and Gamma bands. The highest classification accuracies were achieved using the support vector machine applied on the PLV features of the main (94.8 %) and independent dataset (100 %)</div></div><div><h3>Conclusions</h3><div>These findings indicate that functional connectivity-based (PLV) features have a good potential to be used as a biomarker of OCD.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011716","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Obsessive-compulsive disorder (OCD) is a potentially serious mental disorder that affects 1–2% of the world population. The OCD patients experience uncontrollable, and recurring thoughts (obsessions), and may feel a need to repeat behaviors (compulsions). In EEG studies, many different features have been investigated regarding their OCD diagnosis capability. However, there is no OCD study evaluating different EEG features with the same conditions and data.
Methods
To address the problem, we employed six popular resting-state EEG features, including absolute and relative power, Phase locking value (PLV), Weighted phase lag index (WPLI), approximate entropy, and Higuchi’s fractal dimension, to find out which feature can better discriminate OCDs from healthy controls (CON) under the same conditions and data. All the generated features were normalized using mean and standard deviation of values, calculated from 233 Iranian healthy people. After that, the most informative EEG features, discriminating 39 OCD individuals from age, handedness, and gender-matched, 39 CON were selected and entered into the classification process. In addition, an independent EEG dataset including 23 OCDs and 23 CONs was also used to investigate the consistency of the results.
Results
As expected, most of the significant differences were observed at the high frequency bands in Beta I-IV, and Gamma bands. The highest classification accuracies were achieved using the support vector machine applied on the PLV features of the main (94.8 %) and independent dataset (100 %)
Conclusions
These findings indicate that functional connectivity-based (PLV) features have a good potential to be used as a biomarker of OCD.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.