Agarwal Megha, Singhal Amit
Department of Electronics & Communication Engineering, Jaypee Institute of Information Technology, Noida, India.
Department of Electronics & Communication Engineering, Netaji Subhas University of Technology, Delhi, India.
Sleep Med. 2024 Dec;124:282-288. doi: 10.1016/j.sleep.2024.09.025. Epub 2024 Sep 24.
Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP cycles. In this work, we develop an easy-to-implement accurate system to differentiate between CAP A and CAP B. Small segments of the EEG signal are processed using Gaussian filters to obtain sub-band components. Features are extracted using some statistical characteristics of these signal components. Minimum redundancy maximum relevance test is employed to identify the more significant features. Three different machine learning classifiers are considered and their performance is compared. The results are analyzed for both the balanced and unbalanced datasets. The k-nearest neighbour (kNN) classifier achieves 79.14 % accuracy and F-1 score of 79.24 % for the balanced dataset. The proposed method outperforms the existing methods for CAP classification. It is easy-to-implement and can be considered as a candidate for real-time deployment.
周期性交替模式（CAP）出现在非快速眼动睡眠期间的脑电图（EEG）信号中。对CAP的分析可以为各种睡眠障碍提供见解。第一步是识别CAP周期的A相和B相。在这项工作中，我们开发了一个易于实现的精确系统来区分CAP A和CAP B。使用高斯滤波器处理EEG信号的小段以获得子带分量。利用这些信号分量的一些统计特征提取特征。采用最小冗余最大相关性测试来识别更显著的特征。考虑了三种不同的机器学习分类器并比较了它们的性能。对平衡和不平衡数据集的结果都进行了分析。对于平衡数据集，k近邻（kNN）分类器的准确率达到79.14%，F-1分数达到79.24%。所提出的方法在CAP分类方面优于现有方法。它易于实现，可以被视为实时部署的候选方法。
