一种有效的睡眠循环交替模式阶段分类系统。

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-19 DOI:10.1007/s11571-025-10261-x
Megha Agarwal, Amit Singhal
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引用次数: 0

摘要

脑电图(EEG)信号是分析睡眠模式的常用工具。循环交替模式(CAP)可以在无意识睡眠期间的脑电图信号中观察到。对CAP的详细研究有助于许多睡眠障碍的早期诊断。首先,CAP周期需要划分为它们的组成部分,即阶段A和阶段B。在这项工作中,我们开发了一个准确且易于实现的系统来区分两个CAP阶段。脑电图信号被去噪并分成更小的片段,以便于处理。使用零相位滤波将这些片段分解成不同的频率子带。然后,从子带分量中提取统计特征,并使用Kruskal-Wallis检验选择显著特征。我们考虑了四种不同的分类算法,即k近邻(kNN),支持向量机(SVM),袋装树(BT)和神经网络(NN)。分类结果是为包括健康受试者和失眠患者在内的数据集编制的。BT分类器在组合平衡数据集上产生了最好的结果,准确率为83.29%,F-1得分为83.58%。该方法比现有方案更准确、更高效,可考虑在实际场景中广泛部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient system for classifying cyclic alternating pattern phases in sleep.

Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating patterns (CAP) can be observed in EEG signals during unconscious periods of sleep. Detailed study of CAP can help in early diagnosis of many sleep disorders. Firstly, the CAP cycles need to be segregated into their constituents, phase A and phase B periods. In this work, we develop an accurate and easy-to-implement system to distinguish between the two CAP phases. The EEG signals are denoised and divided into smaller segments for an easier processing. These segments are decomposed into different frequency sub-bands using zero-phase filtering. Thereafter, statistical features are extracted from the sub-band components, and significant features are selected using the Kruskal-Wallis test. We consider four different algorithms for classification, namely, k-nearest neighbour (kNN), support vector machine (SVM), bagged tree (BT) and neural network (NN). The classification results are compiled for the datasets that include healthy subjects and those suffering from insomnia. The BT classifier produces the best results for the combined balanced dataset, with 83.29% accuracy and 83.58% F-1 score. The proposed method is more accurate and efficient than the existing schemes and can be considered for widespread deployments in real-world scenarios.

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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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