Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

L. RadhakrishnanB., K. Ezra, I. Jebadurai
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Abstract

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals.
基于Tsfresh和堆叠集成方法的单通道EEG特征提取及其睡眠阶段分类
工业4.0下的智能世界正在见证睡眠障碍和睡眠相关问题患者的显著激增。人工智能和物联网在将睡眠患者与医疗服务提供者远程连接方面取得了巨大飞跃。当代基于单通道的监测设备在预测睡眠质量和相关问题方面发挥着巨大的作用。在基于机器学习的自动睡眠分类中,手工特征提取是一项耗时的工作。提出的单通道工作使用Tsfresh分别从SEDFEx数据库的两个EEG通道(Pz-oz和Fpz-Cz)中提取特征,以实现单通道EEG。采用mRMR特征选择方法,从提取的787个特征中选择55个特征。在使用健康受试者数据的2、3、4和5类分类中,使用分层5倍验证,堆叠集成分类器实现了95%、94%、91%和88%的准确率。实验结果表明,Tsfresh是一种很好的提取脑电信号标准特征的工具。
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