Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Brain Behav. 2023 Jun;13(6):e3028. doi: 10.1002/brb3.3028. Epub 2023 May 18.
Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression.
An efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters.
This method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals.
According to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.
在睡眠中检测觉醒事件是一项具有挑战性、耗时且昂贵的工作，需要具备神经学知识。尽管类似的自动化系统专门用于检测睡眠阶段，但早期检测睡眠事件有助于识别神经病理学的进展。
本文首次提出了一种使用单导联脑电图（EEG）信号高效识别和评估觉醒事件的混合深度学习方法。该方法采用了 Inception-ResNet-v2 学习迁移模型和优化的支持向量机（SVM）与径向基函数（RBF）核，能够以低于 8%的最小错误水平进行分类。除了保持准确性外，Inception 模块和 ResNet 还显著降低了 EEG 信号中觉醒事件检测的计算复杂度。此外，为了提高 SVM 的分类性能，灰狼算法（GWO）优化了其核参数。
该方法已使用 2018 年 Challenge Physiobank 睡眠数据集的预处理样本进行验证。除了降低计算复杂度外，该方法的结果表明，特征提取和分类的不同部分在识别睡眠障碍方面是有效的。所提出的模型检测睡眠觉醒事件的平均准确率为 93.82%。在识别中存在导联的情况下，该方法在记录人们的 EEG 信号时变得不那么激进。
根据这项研究，所提出的策略在检测睡眠障碍临床试验中的觉醒事件是有效的，可用于睡眠障碍检测诊所。
