Tautan Alexandra-Maria, Rossi Alessandro C, de Francisco Ruben, Ionescu Bogdan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5330-5334. doi: 10.1109/EMBC44109.2020.9175628.
Automatic sleep stage detection can be performed using a variety of input signals from a polysomnographic (PSG) recording. In this study, we investigate the effect of different input signals on the performance of feature-based automatic sleep stage classification algorithms with both a Random Forest (RF) and Multilayer Perceptron (MLP) classifier. Combinations of the EEG (electroencephalographic) signal and ECG (electrocardiographic), EMG (electromyographic) and respiratory signals as input are investigated as input with respect to using single channel and multi-channel EEG as input. The Physionet "You Snooze, You Win" dataset is used for the study. The RF classifier consistently outperforms our MLP implementation in all cases and is positively affected by specific signal combinations. The overall classification performance using a single channel EEG is high (an accuracy, precision and recall of 86.91 %, 89.52%, 86.91% respectively) using RF. The results are comparable to the performance obtained using six EEG channels as input. Adding respiratory signals to the inputs processed by RF increases the N2 stage detection performance with 20%, while adding the EMG signal improves the accuracy of the REM stage detection with 5%. Our analysis shows that adding specific signals as input to RF improves the accuracy of specific sleep stages and increases the overall performance. Using a combination of EEG and respiratory signals we achieved an accuracy of 93% for the RF classifier.
自动睡眠阶段检测可以使用多导睡眠图（PSG）记录中的各种输入信号来执行。在本研究中，我们研究了不同输入信号对基于特征的自动睡眠阶段分类算法性能的影响，该算法使用随机森林（RF）和多层感知器（MLP）分类器。研究了脑电图（EEG）信号与心电图（ECG）、肌电图（EMG）和呼吸信号的组合作为输入，与使用单通道和多通道EEG作为输入的情况进行对比。使用Physionet的“You Snooze, You Win”数据集进行研究。在所有情况下，RF分类器始终优于我们的MLP实现，并且受到特定信号组合的积极影响。使用RF时，单通道EEG的总体分类性能很高（准确率、精确率和召回率分别为86.91%、89.52%、86.91%）。结果与使用六个EEG通道作为输入所获得的性能相当。将呼吸信号添加到由RF处理的输入中，可使N2阶段检测性能提高20%，而添加EMG信号可使快速眼动（REM）阶段检测的准确率提高5%。我们的分析表明，向RF添加特定信号作为输入可提高特定睡眠阶段的准确率并提高总体性能。对于RF分类器，使用EEG和呼吸信号的组合，我们实现了93%的准确率。
