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精神活动期间精神分裂症的脑电图功率谱分析

EEG power spectrum analysis for schizophrenia during mental activity.

作者信息

Thilakavathi B, Shenbaga Devi S, Malaiappan M, Bhanu K

机构信息

Department of ECE, Rajalakshmi Engineering College, Chennai, 602105, India.

Department of ECE, College of Engineering, Guindy, Chennai, India.

出版信息

Australas Phys Eng Sci Med. 2019 Sep;42(3):887-897. doi: 10.1007/s13246-019-00779-w. Epub 2019 Jul 30.

Abstract

Cognitive dysfunction is a core defect for schizophrenia subjects. This is due to structural and functional abnormalities of the brain which can be determined using Electroencephalogram (EEG). The objective of this study is to analyze EEG in patients with schizophrenia using power spectral density during mental activity. The subjects included in this study are 52 schizophrenia subjects and 29 Normal subjects. EEG is recorded under resting condition and during mental activity. Two modified odd ball paradigms are designed to stimulate mental activity and named as stimulus 1 and stimulus 2. EEG signal is filtered using FIR band pass filter to extract delta, theta, alpha, and beta band EEG. This method measures powers of each band using Welch power spectral density method called absolute power. The absolute power of alpha band is low and beta band is high for schizophrenia subjects compared to normal subjects during rest and two stimuli. Student's t-test is used to find the significant features (p < 0.05) at each recording condition. The significant features from each recording condition are used to classify Schizophrenia using both BPN and SVM classifier. SVM classifier is produced maximum sensitivity of 91% when features from all recording conditions are combined together. Thus this work concludes that the mental activity EEG supports for classifying Schizophrenia from normal and hence absolute band powers can be used as features to identify Schizophrenia.

摘要

认知功能障碍是精神分裂症患者的核心缺陷。这是由于大脑的结构和功能异常所致,可通过脑电图(EEG)来确定。本研究的目的是在精神活动期间使用功率谱密度分析精神分裂症患者的脑电图。本研究纳入的受试者为52名精神分裂症患者和29名正常受试者。在静息状态和精神活动期间记录脑电图。设计了两种改良的odd ball范式来刺激精神活动,分别命名为刺激1和刺激2。使用FIR带通滤波器对脑电图信号进行滤波,以提取δ、θ、α和β波段脑电图。该方法使用称为绝对功率的韦尔奇功率谱密度法测量每个波段的功率。在静息状态和两种刺激期间,与正常受试者相比,精神分裂症患者的α波段绝对功率较低,β波段绝对功率较高。使用学生t检验在每个记录条件下找出显著特征(p < 0.05)。每个记录条件下的显著特征用于使用BPN和SVM分类器对精神分裂症进行分类。当将所有记录条件下的特征组合在一起时,SVM分类器产生的最大灵敏度为91%。因此,这项工作得出结论,精神活动脑电图有助于从正常人群中区分出精神分裂症患者,因此绝对波段功率可作为识别精神分裂症的特征。

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