Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-09-19 DOI:10.1007/s12021-024-09684-4
Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore
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Abstract

Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinking, perception, and behavior. Early identification of schizophrenia can help prevent potential complications and facilitate effective treatment and management of the condition. This paper proposes a computer aided diagnosis system for the early detection of schizophrenia using 19-channel Electroencephalography (EEG) signals from 28 subjects, leveraging statistical moments of Mel-frequency Cepstral Coefficients (MFCC) and ensemble learning. Initially, the EEG signals are passed through a high-pass filter to mitigate noise and remove extraneous data. The feature extraction technique is then employed to extract MFC coefficients from the filtered EEG signals. The dimensionality of these coefficients is reduced by computing their statistical moments, which include the mean, standard deviation, skewness, kurtosis, and energy. Subsequently, the Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is applied to identify pertinent features from the statistical moments of the MFC coefficients. These SVM-RFE-based selected features serve as input for three base classifiers: Support Vector Machine, k-Nearest Neighbors, and Logistic Regression. Additionally, an ensemble learning approach, which combines the predictions of the three classifiers through majority voting, is introduced to enhance schizophrenia detection performance and generalize the results of the proposed approach. The study’s findings demonstrate that the ensemble model, combined with SVM-RFE-based selected statistical moments of MFCC, achieves encouraging detection performance, highlighting the potential of machine learning techniques in advancing the diagnostic process of schizophrenia.

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利用 MFC 系数的选定统计矩和集合学习从脑电图信号中检测精神分裂症
精神分裂症是一种精神障碍,其特征是神经生理功能失调,导致思维、感知和行为紊乱。早期发现精神分裂症有助于预防潜在的并发症,并促进有效的治疗和管理。本文提出了一种计算机辅助诊断系统,利用 Mel-frequency Cepstral Coefficients (MFCC) 的统计矩和集合学习,通过 28 名受试者的 19 个通道的脑电图(EEG)信号,对精神分裂症进行早期检测。首先,脑电信号经过高通滤波器,以减少噪音和去除无关数据。然后采用特征提取技术从滤波后的脑电信号中提取 MFC 系数。通过计算这些系数的统计矩(包括平均值、标准偏差、偏斜度、峰度和能量)来降低其维度。随后,应用基于支持向量机的递归特征消除(SVM-RFE)从 MFC 系数的统计矩中识别相关特征。这些基于 SVM-RFE 的选定特征可作为三个基础分类器的输入:支持向量机、k-近邻和逻辑回归。此外,还引入了一种集合学习方法,通过多数投票将三个分类器的预测结果结合起来,以提高精神分裂症的检测性能,并推广所提议方法的结果。研究结果表明,集合模型结合基于 SVM-RFE 的 MFCC 选定统计矩,取得了令人鼓舞的检测性能,凸显了机器学习技术在推进精神分裂症诊断过程中的潜力。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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