Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Sixty-five young (30.0 8.6 yrs.) and healthy volunteers underwent nocturnal PSG and IR-UWB radar measurement simultaneously; From 51 recordings, 26 were used for training, 8 for validation, and 17 for testing. Sixteen features including movement-, respiration-, and heart rate variability-related indices were extracted from the raw IR-UWB signals in each 30-s epoch. Sleep stage classification performances of Attention Bi-LSTM model with optimized hyperparameters were evaluated and compared with those of conventional LSTM networks for same test dataset. In the results, we achieved an accuracy of 82.6 6.7% and a Cohen's kappa coefficient of 0.73 0.11 in the classification of wake stage, REM sleep, light (N1+N2) sleep, and deep (N3) sleep which is significantly higher than the conventional LSTM networks (p < 0.01). Moreover, the classification performances were higher than those reported in comparative studies, demonstrating the effectiveness of the attention mechanism coupled with bi-LSTM networks for the sleep staging using cardiorespiratory signals.

中文翻译：

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基于注意的LSTM，用于使用IR-UWB雷达进行非接触式睡眠阶段分类。

从多导睡眠图（PSG）记录对睡眠阶段进行人工评分对于了解睡眠质量和结构至关重要。由于PSG需要专业人员，实验室环境和不舒适的传感器，因此在过去几年中，已经研究了基于机器学习技术的非接触式睡眠分期方法。在这项研究中，我们提出了一种基于注意力的双向长期短期记忆（Attention Bi-LSTM）模型，该模型可使用脉冲无线电超宽带（IR-UWB）雷达进行自动睡眠阶段评分，该雷达可以远程检测生命体征。65名年轻（30.0 8.6岁）和健康志愿者同时进行了夜间PSG和IR-UWB雷达测量；从51个记录中，有26个用于训练，8个用于验证，17个用于测试。十六种功能包括运动，呼吸，在每个30秒的时间段内，从原始的IR-UWB信号中提取与心率变异性相关的指标。对具有优化超参数的Attention Bi-LSTM模型的睡眠阶段分类性能进行了评估，并将其与传统LSTM网络的相同测试数据集进行了比较。结果表明，在唤醒阶段，REM睡眠，轻度（N1 + N2）睡眠和深度（N3）睡眠的分类中，我们的准确度为82.6 6.7％，科恩氏kappa系数为0.73 0.11，显着高于睡眠阶段。常规LSTM网络（p <0.01）。此外，分类性能高于比较研究中报告的分类性能，证明了注意机制与bi-LSTM网络结合使用心肺信号进行睡眠分期的有效性。