AI-Driven sleep staging from actigraphy and heart rate.
AI-Driven sleep staging from actigraphy and heart rate.
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AI驱动的睡眠分阶段是由动作学和心律进行的。
DOI：
10.1371/journal.pone.0285703
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
Dutta, Joyita
中科院分区：
文献类型：
--
作者：
Song, Tzu-An;Chowdhury, Samadrita Roy;Malekzadeh, Masoud;Harrison, Stephanie;Hoge, Terri Blackwell;Redline, Susan;Stone, Katie L.;Saxena, Richa;Purcell, Shaun M.;Dutta, Joyita
睡眠是一个人健康的重要指标，其准确且经济有效的量化在医疗保健中具有重要价值。睡眠评估和睡眠障碍临床诊断的金标准是多导睡眠图（PSG）。但是，PSG需要夜间门诊访视和经过培训的技术人员对获得的多模态数据进行评分。智能手表等腕戴式消费者设备是PSG的一种有前途的替代品，因为它们的外形尺寸小，具有连续监测能力，并且很受欢迎。然而，与PSG不同的是，可穿戴设备产生的数据噪音更大，信息量也少得多，因为它们的形状因子小，模态数量少，测量精度也不高。鉴于这些挑战，大多数消费者设备都执行两阶段（即，睡眠-觉醒）分类，这不足以深入了解一个人的睡眠健康。使用来自腕戴式可穿戴设备的数据进行具有挑战性的多级（三级、四级或五级）睡眠分期仍然没有得到解决。消费级可穿戴设备和实验室级临床设备之间的数据质量差异是这项研究背后的动机。在这篇论文中，我们提出了一种人工智能（AI）技术，称为自动移动的睡眠分期（SLAMSS）的序列到序列LSTM，它可以从活动（即，腕部加速度计导出的运动）和两个粗略的心率测量-这两者都可以从消费级腕部可穿戴设备可靠地获得。我们的方法依赖于原始的时间序列数据集，避免了手动特征选择的需要。我们使用来自两个独立研究人群的体动计和粗略心率数据验证了我们的模型：多种族动脉粥样硬化研究（梅萨; N = 808）队列和男性骨质疏松性骨折（MrOS; N = 817）队列。在梅萨队列中，SLAMSS对三级睡眠分期的总体准确性为79%，加权F1评分为0.80，敏感性为77%，特异性为89%，对四级睡眠分期的总体准确性为70- 72%，加权F1评分为0.72-0.73，敏感性为64-66%，特异性为89-90%。在MrOS队列中，三级睡眠分期的总体准确性为77%，加权F1评分为0.77，敏感性为74%，特异性为88%，四级睡眠分期的总体准确性为68- 69%，加权F1评分为0.68-0.69，敏感性为60-63%，特异性为88-89%。这些结果是用具有低时间分辨率的特征差的输入实现的。此外，我们将三类分期模型扩展到了一个不相关的Apple Watch数据集。重要的是，SLAMSS以高精度预测每个睡眠阶段的持续时间。这对于四类睡眠阶段尤其重要，因为深度睡眠严重不足。我们表明，通过适当地选择损失函数来解决固有的类别不平衡，我们的方法可以准确地估计深睡眠时间（SLAMSS/梅萨：0.61±0.69小时，PSG/梅萨地面实况：0.60±0.60小时; SLAMSS/MrOS：0.53±0.66小时，PSG/MrOS地面实况：0.55±0.57小时;）。深度睡眠的质量和数量是许多疾病的重要指标和早期指标。因此，我们的方法可以从可穿戴设备获得的数据中准确估计深度睡眠，对于需要长期深度睡眠监测的各种临床应用来说是有希望的。
Sleep is an important indicator of a person’s health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person’s sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures—both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.
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影响因子：
29.9
作者：
Djonlagic, Ina;Mariani, Sara;Fitzpatrick, Annette L.;Van der Klei, Veerle M. G. T. H.;Johnson, Dayna A.;Wood, Alexis C.;Seeman, Teresa;Nguyen, Ha T.;Prerau, Michael J.;Luchsinger, Jose A.;Dzierzewski, Joseph M.;Rapp, Stephen R.;Tranah, Gregory J.;Yaffe, Kristine;Burdick, Katherine E.;Stone, Katie L.;Redline, Susan;Purcell, Shaun M.
通讯作者：
Purcell, Shaun M.
利用无线可穿戴传感器自动进行睡眠阶段分类
影响因子：
15.2
作者：
Boe, Alexander J.;Koch, Lori L. McGee;Jayaraman, Arun
通讯作者：
Jayaraman, Arun
影响因子：
3.7
作者：
Delgado, Rosario;Tibau, Xavier-Andoni
通讯作者：
Tibau, Xavier-Andoni
影响因子：
3.2
作者：
Beattie, Z.;Oyang, Y.;Heneghan, C.
通讯作者：
Heneghan, C.
影响因子：
5.6
作者：
Dashti, Hassan S.;Redline, Susan;Saxena, Richa
通讯作者：
Saxena, Richa