Chazal Philip de, Sadr Nadi
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2796-2799. doi: 10.1109/EMBC44109.2020.9175290.
A system for automated annotation of selected signals from the polysomnogram (PSG) for the presence of apnoea and non-apnoea arousals is presented. Fifty nine time- and frequency-domain features were derived from the PSG for each 15 second epoch and after combining features from adjacent epochs, the feature information was processed with a bank of feed-forward neural networks that provided a probability estimate that each epoch was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using volume under the receiver operator characteristic surface (VUROS) as well as no-arousal specificity and arousal sensitivities. Using a bank of ten feed-forward neural networks with each network processing ±4 epochs of features and each used a single hidden layer of 20 units, the system achieved a VUROS of 0.73 with a specificity of 70%, a sensitivity of 75% for the apnoea arousals, and a sensitivity of 70% for the non-apnoea arousals.
提出了一种用于自动注释多导睡眠图（PSG）中选定信号以检测呼吸暂停和非呼吸暂停唤醒的系统。对于每个15秒的时段，从PSG中提取了59个时域和频域特征，在组合相邻时段的特征后，使用一组前馈神经网络对特征信息进行处理，该网络提供每个时段与呼吸暂停、非呼吸暂停唤醒或无唤醒相关联的概率估计。使用来自2018年心脏病学挑战赛Physionet计算的数据来开发和测试该系统。使用接收器操作特征表面下的体积（VUROS）以及无唤醒特异性和唤醒敏感性来评估系统的性能。该系统使用一组十个前馈神经网络，每个网络处理±4个时段的特征，每个网络使用一个包含20个单元的单隐藏层，实现了0.73的VUROS，特异性为70%，呼吸暂停唤醒的敏感性为75%，非呼吸暂停唤醒的敏感性为70%。
