Nateghi Masoud, Rahbar Alam Mahdi, Amiri Hossein, Nasiri Samaneh, Sameni Reza
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
Independent Researcher, Shiraz 7197688711, Iran.
Sensors (Basel). 2024 Dec 10;24(24):7881. doi: 10.3390/s24247881.
Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep's impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sleep analysis is detecting transitions between wakefulness and sleep states. These transitions offer valuable insights into sleep quality and quantity, essential for diagnosing sleep disorders, designing effective interventions, enhancing overall health and well-being, and studying sleep's effects on cognitive function, mood, and physical performance. This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. We propose a noise-robust model-based Kalman filtering (KF) approach to track changes in a time-varying auto-regressive model (TVAR) applied to EEG data during different wake and sleep stages. Our approach involves extracting features, including instantaneous frequency and instantaneous power from EEG, and implementing a two-step classifier for sleep staging. The first step classifies data into wake, REM, and non-REM categories, while the second step further classifies non-REM data into N1, N2, and N3 stages. Evaluation on the extended Sleep-EDF dataset (Sleep-EDFx), with 153 EEG recordings from 78 subjects, demonstrated compelling results with classifiers including Logistic Regression, Support Vector Machines, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The best performance was achieved with the LGBM and XGBoost classifiers, yielding an overall accuracy of over 77%, a macro-averaged F1 score of 0.69, and a Cohen's kappa of 0.68, highlighting the efficacy of the proposed method with a remarkably compact and interpretable feature set.
了解睡眠阶段对于诊断睡眠障碍、开发治疗方法以及研究睡眠对整体健康的影响至关重要。随着价格亲民的脑监测设备越来越普及，收集到的脑数据量显著增加。然而，分析这些数据，尤其是使用金标准多导联脑电图（EEG）时，仍然资源密集且耗时。为应对这一挑战，自动脑监测已成为经济高效的EEG数据分析的关键解决方案。睡眠分析的一个关键组成部分是检测清醒和睡眠状态之间的转换。这些转换为睡眠质量和数量提供了有价值的见解，对于诊断睡眠障碍、设计有效的干预措施、增进整体健康和幸福感以及研究睡眠对认知功能、情绪和身体表现的影响至关重要。本研究提出了一种新颖的EEG特征提取流程，用于准确分类各种清醒和睡眠阶段。我们提出了一种基于噪声鲁棒模型的卡尔曼滤波（KF）方法，以跟踪应用于不同清醒和睡眠阶段的EEG数据的时变自回归模型（TVAR）中的变化。我们的方法包括从EEG中提取特征，包括瞬时频率和瞬时功率，并实施用于睡眠分期的两步分类器。第一步将数据分类为清醒、快速眼动（REM）和非快速眼动类别，而第二步将非快速眼动数据进一步分类为N1、N2和N3阶段。对扩展的睡眠-EDF数据集（Sleep-EDFx）进行评估，该数据集包含来自78名受试者的153份EEG记录，使用包括逻辑回归、支持向量机、极端梯度提升（XGBoost）和轻量级梯度提升机（LGBM）在内的分类器取得了令人信服的结果。LGBM和XGBoost分类器表现最佳，总体准确率超过77%，宏平均F1分数为0.69，科恩卡方系数为0.68，突出了所提出方法在具有非常紧凑且可解释的特征集时的有效性。
