استخراج نحوه تغییر رفتار مغز بیماران مبتال به میگرن بهوسیله تحلیل کمی سیگنال الکتروانسفالوگرام بیماران در مقایسه با افراد سالم.
Background. Migraine disease is the second most common cause of headaches. Despite the high prevalence, the exact etiology of migraine is yet unknown. In this study, to evaluate the behavior change of electroencephalography (EEG) signals in migraine patients, various features of the EEG signals of m...
| Publicado en: | Medical Journal of Tabriz University of Medical Sciences Vol. 46; no. 1; pp. 48 - 61 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Tabriz University of Medical Sciences
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176771972&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176771972 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27832031 N3KB jtl: Medical Journal of Tabriz University of Medical Sciences issn: 27832031 maglogo: N pubinfo: dt: Apr2024 vid: 46 iid: 1 pid: 69573 pub: Tabriz University of Medical Sciences artinfo: ui: 176771972 176771972 176771972 10.34172/mj.2024.013 176771972 ppf: 48 ppct: 13 formats: fmt: @attributes: type: P tig: atl: استخراج نحوه تغییر رفتار مغز بیماران مبتال به میگرن بهوسیله تحلیل کمی سیگنال الکتروانسفالوگرام بیماران در مقایسه با افراد سالم. aug: au: یاشار سرباز فرناز قرهداغی سعید مشگینی affil: آزمایشگاه مدلساز ی سیستمهای بیولوژیکی، گروه مهندسی پزشکی، دانشکده برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران. sug: subj: Migraine Behavioral Changes Electroencephalography Brain Waves Signal Processing, Computer Assisted Quantitative Studies Cross Sectional Studies Human Male Young Adult Adult Middle Age Comparative Studies Nervous System Diseases Neurosciences Adult: 19-44 years Middle Aged: 45-64 years Male ab: Background. Migraine disease is the second most common cause of headaches. Despite the high prevalence, the exact etiology of migraine is yet unknown. In this study, to evaluate the behavior change of electroencephalography (EEG) signals in migraine patients, various features of the EEG signals of migraine patients and healthy controls (HCs) were extracted and compared. Methods. This cross-sectional analytical study was conducted on 21 HCs and 18 migraine patients. Various features, such as fractal dimension (FD), approximate entropy (ApEn), and largest Lyapunov exponent (LLE), were calculated from the EEG signals of migraine patients and HCs. Then different frequency sub-bands of delta, theta, alpha, beta, and gamma were extracted using the wavelet transform, and the energy of these sub-bands was computed. By calculating the mean and variance of the features and applying statistical tests, the feature changes were compared between two groups, and channels with significant differences were identified. Results. The mean of ApEn, FD and energy of all frequency sub-bands in most of the analyzed channels was higher in migraine patients than in HCs. The mean LLE was mostly lower in migraine patients than in healthy controls. According to the statistical tests, the energy of theta and delta frequency sub-bands with 36 and 35 channels was the feature with the highest number of channels, with a significant difference. In this study, P values less than 0.05 were considered statistically significant. Conclusion. Migraine patients may have a less sophisticated brain dynamic system due to an increase in irregularity and randomness, as indicated by an increase in ApEn and a decrease in FD in their EEG signals compared to HCs. Anxiety, tension, and other intense sentiments and emotions, as well as the creation of new neural circuits in the brain, can all contribute to an overall increase in energy across all frequency sub-bands in migraine patients. Practical Implications. Considering the EEG signal behavior as the response of a dynamic system, we can say that the brain function of migraine patients, even in the inter-ictal phase, leaves the definite chaotic state, which is a healthy brain behavior, and enters the random state. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: Persian refInfo: holdings: @attributes: islocal: N |
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