Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.
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机译:使用心率变异性(HRV)的自动睡眠阶段分类可以为金标准多导睡眠图提供一种人体工程学且低成本的替代方案,从而为基于家庭的睡眠监测提供了可能。但是,当前的方法在考虑长期睡眠体系结构模式方面的能力有限。提出了一种长期短期记忆(LSTM)网络,作为对长期心脏睡眠结构信息进行建模的解决方案,并在包括广泛范围内的广泛数据集(292名参与者,584晚,541.214标注的30 s睡眠段)上进行了验证。年龄和病理特征,根据Rechtschaffen and Kales(R&K)注释标准进行注释。结果表明,该模型的性能优于最先进的方法,该方法通常仅限于非时间或短期递归分类器。该模型在整个数据库中的Cohen k值为0.61±0.15,准确度为77.00±8.90%。进一步的分析表明,年龄在50岁以上的人的表现可能会下降。这些结果证明了使用多种数据集进行深度时态建模的优点,并推动了基于HRV的睡眠阶段分类的最新技术。由于该人群的性能趋于恶化,因此有必要对50岁以上的人进行进一步的研究。
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