Mohammad Farah, Al Mansoor Khulood Mohammed
Department of Computer Science and Technology, Arab East Colleges, Riyadh 11583, Saudi Arabia.
Self-Development Skills Department, King Saud University, Riyadh 11543, Saudi Arabia.
Diagnostics (Basel). 2024 Nov 25;14(23):2657. doi: 10.3390/diagnostics14232657.
: Sleep stage analysis is considered to be the key factor for understanding and diagnosing various sleep disorders, as it provides insights into sleep quality and overall health. : Traditional methods of sleep stage classification, such as manual scoring and basic machine learning approaches, often suffer from limitations including subjective biases, limited scalability, and inadequate accuracy. Existing deep learning models have improved the accuracy of sleep stage classification but still face challenges such as overfitting, computational inefficiencies, and difficulties in handling imbalanced datasets. To address these challenges, we propose the Sleep Stage Analysis with Multilayer Perceptron (SLA-MLP) model. : SLA-MLP leverages advanced deep learning techniques to enhance the classification of sleep stages from EEG signals. The key steps of this approach include data collection, where diverse and high-quality EEG data are gathered; preprocessing, which involves signal cropping, spectrogram conversion, and normalization to prepare the data for analysis; data balancing, where class weights are adjusted to address any imbalances in the dataset; feature extraction, utilizing Temporal Convolutional Networks (TCNs) to extract meaningful features from the EEG signals; and final classification, applying a Multilayer Perceptron (MLP) to accurately predict sleep stages. SLA-MLP demonstrates superior performance compared to traditional methods by effectively addressing the limitations of existing models. Its robust preprocessing techniques, advanced feature extraction, and adaptive data balancing strategies collectively contribute to obtaining more accurate results, having an accuracy of 97.23% for the S-DSI, 96.23 for the S-DSII and 97.23% for the S-DSIII dataset. This model offers a significant advancement in the field, providing a more precise tool for sleep research and clinical applications.
睡眠阶段分析被认为是理解和诊断各种睡眠障碍的关键因素，因为它能洞察睡眠质量和整体健康状况。传统的睡眠阶段分类方法，如人工评分和基本的机器学习方法，往往存在局限性，包括主观偏差、可扩展性有限和准确性不足。现有的深度学习模型提高了睡眠阶段分类的准确性，但仍面临诸如过拟合、计算效率低下以及处理不平衡数据集困难等挑战。为应对这些挑战，我们提出了多层感知器睡眠阶段分析（SLA - MLP）模型。SLA - MLP利用先进的深度学习技术来增强从脑电图信号中对睡眠阶段的分类。该方法的关键步骤包括数据收集，即收集多样且高质量的脑电图数据；预处理，包括信号裁剪、频谱图转换和归一化，为分析准备数据；数据平衡，即调整类别权重以解决数据集中的任何不平衡问题；特征提取，利用时间卷积网络（TCN）从脑电图信号中提取有意义的特征；以及最终分类，应用多层感知器（MLP）准确预测睡眠阶段。与传统方法相比，SLA - MLP通过有效解决现有模型的局限性展现出卓越性能。其强大的预处理技术、先进的特征提取和自适应数据平衡策略共同有助于获得更准确的结果，在S - DSI数据集上的准确率为97.23%，在S - DSII数据集上为96.23%，在S - DSIII数据集上为97.23%。该模型在该领域取得了重大进展，为睡眠研究和临床应用提供了更精确的工具。
