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首页> 外文会议>International Conference on Advance Computing and Innovative Technologies in Engineering >Automated Sleep Staging Analysis using Sleep EEG signal: A Machine Learning based Model
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Automated Sleep Staging Analysis using Sleep EEG signal: A Machine Learning based Model

机译：自动睡眠分期分析使用睡眠EEG信号：基于机器学习的模型

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摘要

Sleep is essential for people health and well-being. However, numerous individuals face sleep problems. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. Artificial intelligence methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main contribution of this paper is to present the design and development of an ASSC. This study supports the recognition of sleep stages and provides relevant information on the sleep process according to American Academy of Sleep Medicine manuals. The proposed method includes a two-step execution process. On the one hand, the sleep records are extracted through electroencephalogram signals. Three different health condition subjects of distinct gender and different age groups have been analyzed. On the other hand, different session recordings of sleep processes from two additional nights are considered. The proposed work uses a single channel for two-state sleep stage classification. This study uses a public dataset and incorporates data pre-processing, data extraction, and feature selection. The entire experiment was executed on different medical conditioned subjects using a support vector machine (SVM). The reported results signify that the proposed model achieved the best classification accuracy of 97.73% with subgroup-I subject using SVM classification models, respectively.
机译：睡眠对人们健康和幸福来说至关重要。然而，众多人面临睡眠问题。这些问题可能导致几种神经系统和身体疾病疾病，因此降低了整体生活质量。自动睡眠阶段分类（ASSC）的人工智能方法是评估和治疗这一公共卫生挑战的基本方法。本文的主要贡献是展示ASSC的设计和开发。本研究支持休眠阶段的识别，并根据美国睡眠医学手册的睡眠过程提供相关信息。该方法包括两步执行过程。一方面，通过脑电图信号提取睡眠记录。分析了三种不同的性别和不同年龄组的不同健康状况。另一方面，考虑了两个额外夜晚的睡眠过程的不同会议记录。建议的工作使用单个通道进行两州睡眠阶段分类。本研究使用公共数据集并包含数据预处理，数据提取和特征选择。使用支持向量机（SVM）在不同的医疗调节受试者上执行整个实验。据报道的结果表明，拟议的模型分别使用SVM分类模型与子组-I拍摄的最佳分类准确性为97.73％。

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