Different biological signals are recorded in sleep labs during sleep for the diagnosis and treatment of human sleep problems. Classification of sleep stages with electroencephalography (EEG) is preferred to other biological signals due to its advantages such as providing clinical information, cost-effectiveness, comfort, and ease of use. The evaluation of EEG signals taken during sleep by clinicians is a tiring, time-consuming, and error-prone method. Therefore, it is clinically mandatory to determine sleep stages by using software-supported systems. Like all classification problems, the accuracy rate is used to compare the performance of studies in this domain, but this metric can be accurate when the number of observations is equal in classes. However, since there is not an equal number of observations in sleep stages, this metric is insufficient in the evaluation of such systems. For this purpose, in recent years, Cohen’s kappa coefficient and even the sensitivity of NREM1 have been used for comparing the performance of these systems. Still, none of them examine the system from all dimensions. Therefore, in this study, two new metrics based on the polygon area metric, called the normalized area of sensitivity polygon and normalized area of the general polygon, are proposed for the performance evaluation of sleep staging systems. In addition, a new sleep staging system is introduced using the applications offered by the MATLAB program. The existing systems discussed in the literature were examined with the proposed metrics, and the best systems were compared with the proposed sleep staging system. According to the results, the proposed system excels in comparison with the most advanced machine learning methods. The single-channel method introduced based on the proposed metrics can be used for robust and reliable sleep stage classification from all dimensions required for real-time applications.

睡眠实验室在睡眠期间记录不同的生物信号，用于诊断和治疗人类睡眠问题。使用脑电图 (EEG) 对睡眠阶段进行分类优于其他生物信号，因为它具有提供临床信息、成本效益、舒适性和易用性等优点。临床医生对睡眠期间采集的脑电图信号进行评估是一种累人、耗时且容易出错的方法。因此，临床上必须使用软件支持的系统来确定睡眠阶段。与所有分类问题一样，准确率用于比较该领域研究的性能，但当类中的观察数量相等时，该指标可能是准确的。然而，由于在睡眠阶段没有相同数量的观察，该指标不足以评估此类系统。为此，近年来，Cohen 的 kappa 系数甚至 NREM1 的灵敏度都被用于比较这些系统的性能。尽管如此，他们都没有从各个方面检查系统。因此，在本研究中，提出了两个基于多边形面积度量的新度量，称为敏感多边形的归一化面积和一般多边形的归一化面积，用于睡眠分期系统的性能评估。此外，使用 MATLAB 程序提供的应用程序引入了一种新的睡眠分级系统。文献中讨论的现有系统使用建议的指标进行了检查，并将最佳系统与建议的睡眠分期系统进行了比较。根据结果​​，与最先进的机器学习方法相比，所提出的系统表现出色。基于所提出的指标引入的单通道方法可用于从实时应用所需的所有维度进行稳健可靠的睡眠阶段分类。