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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使用 IR-UWB 雷达进行基于注意力的 LSTM 非接触式睡眠阶段分类


根据多导睡眠图 (PSG) 记录对睡眠阶段进行手动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，因此过去几年人们对基于机器学习技术的非接触式睡眠分期方法进行了研究。在这项研究中，我们提出了一种基于注意力的双向长短期记忆（Attention Bi-LSTM）模型，使用可以远程检测生命体征的脉冲无线电超宽带（IR-UWB）雷达来自动进行睡眠阶段评分。 65 名年轻（​​30.0 ± 8.6 岁）和健康志愿者同时接受夜间 PSG 和 IR-UWB 雷达测量；在 51 个记录中，26 个用于训练，8 个用于验证，17 个用于测试。从每个 30 秒的原始 IR-UWB 信号中提取了 16 个特征，包括运动、呼吸和心率变异性相关指数。在相同的测试数据集上，评估了具有优化超参数的 Attention Bi-LSTM 模型的睡眠阶段分类性能，并与传统 LSTM 网络的睡眠阶段分类性能进行了比较。结果显示，我们在清醒阶段、快速眼动睡眠、浅度（N1+N2）睡眠和深度（N3）睡眠的分类上取得了 82.6 ± 6.7% 的准确度和 0.73 ± 0.11 的 Cohen's kappa 系数，明显更高比传统的 LSTM 网络 (p < 0.01)。此外，分类性能高于比较研究中报告的性能，证明了注意力机制与双 LSTM 网络相结合对于使用心肺信号进行睡眠分期的有效性。