Wickramaratne Sajila D, Mahmud Md Shaad
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:154-157. doi: 10.1109/EMBC44109.2020.9176413.
Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic detection of sleep arousals is still challenging. In this paper, a novel method is presented to detect non-apnea sources of arousals during sleep using Polysomnography(PSG) recordings. After the preprocessing, a sequence-to-sequence deep neural network (DNNs) consisting of a series of Bidirectional long short-term memory (Bi-LSTM) layer, and fully connected layers were trained to classify samples in the segments. Initially, three different models were prepared for different datasets. Finally, obtaining the classification result through an ensemble model consisting of the three trained models. The result shows that the area under the receiver precision-recall curve (AUPRC) is 0.59 for the test dataset exceeding the performance of the classifiers in the existing literature.Clinical relevance- Analyzing Polysomnographic recordings is a time consuming a critical process yet to identify sleep disorders. These recordings span several hours and contain different data streams that include EEG, EMG, etc. This paper proposes a system that can automatically detect respiratory effort-related arousals using a deep neural network from Polysomnographic Recordings. By automating this process with a machine learning-based solution that can eliminate the manual process while facilitating further improvements of the system with future data.
由于现代生活方式和压力，睡眠障碍变得更加普遍。睡眠障碍最严重的情况是呼吸暂停，其特征是完全阻断，导致觉醒及随后的睡眠干扰。睡眠觉醒的自动检测仍然具有挑战性。本文提出了一种新方法，用于使用多导睡眠图（PSG）记录来检测睡眠期间非呼吸暂停引起的觉醒。经过预处理后，一个由一系列双向长短期记忆（Bi-LSTM）层和全连接层组成的序列到序列深度神经网络（DNN）被训练用于对片段中的样本进行分类。最初，针对不同的数据集准备了三种不同的模型。最后，通过由这三个训练好的模型组成的集成模型获得分类结果。结果表明，测试数据集的接收器精确召回曲线下面积（AUPRC）为0.59，超过了现有文献中分类器的性能。临床相关性——分析多导睡眠图记录是一个耗时且关键的过程，用于识别睡眠障碍。这些记录跨度数小时，包含不同的数据流，包括脑电图（EEG）、肌电图（EMG）等。本文提出了一种系统，该系统可以使用深度神经网络从多导睡眠图记录中自动检测与呼吸努力相关的觉醒。通过基于机器学习的解决方案实现这一过程的自动化，可以消除手动过程，同时便于利用未来的数据进一步改进系统。
