Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
J Clin Neurophysiol. 2012 Feb;29(1):58-64. doi: 10.1097/WNP.0b013e318246b74e.
Arousals occur from all sleep stages and can be identified as abrupt electroencephalogram (EEG) and electromyogram (EMG) changes. Manual scoring of arousals is time consuming with low interscore agreement. The aim of this study was to design an arousal detection algorithm capable of detecting arousals from non-rapid eye movement (REM) and REM sleep, independent of the subject's age and disease. The proposed algorithm uses features from EEG, EMG, and the manual sleep stage scoring as input to a feed-forward artificial neural network (ANN). The performance of the algorithm has been assessed using polysomnographic (PSG) recordings from a total of 24 subjects. Eight of the subjects were diagnosed with Parkinson disease (PD) and the rest (16) were healthy adults in various ages. The performance of the algorithm was validated in 3 settings: testing on the 8 patients with PD using the leave-one-out method, testing on the 16 healthy adults using the leave-one-out method, and finally testing on all 24 subjects using a 4-fold crossvalidation. For these 3 validations, the sensitivities were 89.8%, 90.3%, and 89.4%, and the positive predictive values (PPVs) were 88.8%, 89.4%, and 86.1%. These results are high compared with those of previously presented arousal detection algorithms and especially compared with the high interscore variability of manual scorings.
觉醒发生于所有睡眠阶段，可通过脑电图（EEG）和肌电图（EMG）的突然变化来识别。觉醒的手动评分既耗时又费力，评分者间的一致性较低。本研究旨在设计一种能够从非快速眼动（REM）和 REM 睡眠中检测觉醒的算法，且不依赖于受检者的年龄和疾病。所提出的算法使用 EEG、EMG 和手动睡眠分期评分作为输入，将其馈送至前馈人工神经网络（ANN）。该算法的性能已通过 24 名受试者的多导睡眠图（PSG）记录进行了评估。其中 8 名受试者被诊断为帕金森病（PD），其余 16 名受试者为不同年龄的健康成年人。该算法的性能在 3 种设置下进行了验证：使用留一法在 8 名 PD 患者中进行测试，使用留一法在 16 名健康成年人中进行测试，最后在所有 24 名受试者中使用 4 折交叉验证进行测试。对于这 3 种验证，敏感性分别为 89.8%、90.3%和 89.4%，阳性预测值（PPV）分别为 88.8%、89.4%和 86.1%。与之前提出的觉醒检测算法相比，这些结果较高，尤其是与手动评分的高评分者间变异性相比。
