Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
Department of Electrical Engineering, Indian Institute of Technology, Bombay, Mumbai, India.
Comput Biol Med. 2023 Sep;164:107259. doi: 10.1016/j.compbiomed.2023.107259. Epub 2023 Jul 14.
The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of sleep instability. The CAP can examine various sleep-related disorders. Certain short events (A and B phases) manifest related to a specific physiological process or pathology during non-rapid eye movement (NREM) sleep. These phases unexpectedly modify EEG oscillations; hence, manual detection is challenging. Therefore, it is highly desirable to have an automated system for detecting the A-phases (AP). Deep convolution neural networks (CNN) have shown high performance in various healthcare applications. A variant of the deep neural network called the Wavelet Scattering Network (WSN) has been used to overcome the specific limitations of CNN, such as the need for a large amount of data to train the model. WSN is an optimized network that can learn features that help discriminate patterns hidden inside signals. Also, WSNs are invariant to local perturbations, making the network significantly more reliable and effective. It can also help improve performance on tasks where data is minimal. In this study, we proposed a novel WSN-based CAPSCNet to automatically detect AP using EEG signals. Seven dataset variants of cyclic alternating pattern (CAP) sleep cohort is employed for this study. Two electroencephalograms (EEG) derivations, namely: C4-A1 and F4-C4, are used to develop the CAPSCNet. The model is examined using healthy subjects and patients tormented by six different sleep disorders, namely: sleep-disordered breathing (SDB), insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, periodic leg movement disorder (PLM) and rapid eye movement behavior disorder (RBD) subjects. Several different machine-learning algorithms were used to classify the features obtained from the WSN. The proposed CAPSCNet has achieved the highest average classification accuracy of 83.4% using a trilayered neural network classifier for the healthy data variant. The proposed CAPSCNet is efficient and computationally faster.
循环交替模式（CAP）可被视为睡眠不稳定的生理标志物。CAP 可检查各种与睡眠相关的障碍。在非快速眼动（NREM）睡眠期间，某些短暂事件（A 相和 B 相）与特定的生理过程或病理相关表现。这些阶段会意外地改变 EEG 振荡；因此，手动检测具有挑战性。因此，非常需要一种用于检测 A 相（AP）的自动化系统。深度卷积神经网络（CNN）在各种医疗保健应用中表现出了很高的性能。一种称为小波散射网络（WSN）的深度神经网络变体已被用于克服 CNN 的特定限制，例如需要大量数据来训练模型。WSN 是一种经过优化的网络，可以学习有助于区分隐藏在信号中的模式的特征。此外，WSN 对局部扰动具有不变性，使网络更加可靠和有效。它还可以帮助改善数据最少的任务的性能。在这项研究中，我们提出了一种新的基于 WSN 的 CAPSCNet，用于使用 EEG 信号自动检测 AP。该研究采用了七种循环交替模式（CAP）睡眠队列的数据集变体。使用两种脑电图（EEG）推导，即：C4-A1 和 F4-C4，来开发 CAPSCNet。该模型使用健康受试者和受六种不同睡眠障碍折磨的患者进行检查，这些障碍包括：睡眠呼吸障碍（SDB）、失眠、夜间额叶癫痫（NFLE）、发作性睡病、周期性肢体运动障碍（PLM）和快速眼动行为障碍（RBD）。使用几种不同的机器学习算法对从 WSN 获得的特征进行分类。对于健康数据变体，使用三层神经网络分类器，提出的 CAPSCNet 实现了 83.4%的平均最高分类准确性。提出的 CAPSCNet 效率高，计算速度快。
