标题： 基于卷积神经网络量化心率变异性进行睡眠觉醒分类 显示英文标题

标题： Sleep-wake classification via quantifying heart rate variability by convolutional neural network

摘要： 心率的波动与生物体生理状态的变化密切相关。 我们通过使用个体的瞬时心率（IHR）序列来分类其清醒/睡眠状态，从而研究和利用这种关系。 我们使用卷积神经网络（CNN）从整夜心电图（ECG）提取的IHR序列中构建特征，并每30秒预测一次受试者是清醒还是睡眠。 我们的训练数据库包括56个正常受试者，我们考虑了三个不同的验证数据库；其中一个为私有，两个为公开，具有不同的种族和睡眠呼吸暂停严重程度。 在我们27个受试者的私有数据库上，我们预测清醒阶段的准确率、敏感性、特异性和AUC值分别为83.1%、52.4%、89.4%和0.83。 在我们两个公开数据库上的验证性能相似。 当我们使用光电容积描记法（PPG）而不是ECG来获取IHR序列时，性能也相当。 进行了稳健性检查以确认获得的性能统计数据。 这一结果提倡了一种有效且可扩展的方法，用于通过无创心率监测来识别生理状态的变化。 CNN模型自适应地量化了IHR波动及其在时间中的位置，并适用于区分清醒和睡眠阶段。 显示英文摘要

摘要： Fluctuations in heart rate are intimately tied to changes in the physiological state of the organism. We examine and exploit this relationship by classifying a human subject's wake/sleep status using his instantaneous heart rate (IHR) series. We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 seconds whether the subject is awake or asleep. Our training database consists of 56 normal subjects, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. On our private database of 27 subjects, our accuracy, sensitivity, specificity, and AUC values for predicting the wake stage are 83.1%, 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.