New ubiquitous computing system uniting chaos theory and data science for sleep apnea hypopnea syndrome (SAHS) screening with wearable devices

结合混沌理论和数据科学的新型普适计算系统，用于通过可穿戴设备进行睡眠呼吸暂停低通气综合征 (SAHS) 筛查

• 批准号: 21K17670
• 负责人: 梁 滋路
• 金额: $ 3万
• 依托单位: Kyoto University of Advanced Science
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K17670/
• 关键词: sleep apnea; SpO2; multiscale entropy; machine learning; oximeter; wearable computing; ubiquitous computing; personal informatics; digital health; mHealth; chaos theory

This study aims to develop novel algorithms for SAHS screening with signals that can be collected using consumer wearable devices. We made a major break through this year as we obtained approvals to access two large sleep datasets, i.e., the OSASUD (N=30) and SHHS (N=5768) datasets. We first applied contrast set mining on the OSASUD dataset and found that heart rate and SpO2 were strongly associated with the occurrence of apnea events. We then utilized the SHHS dataset to develop apnea screening models with overnight SpO2 signals and demographic information. We proposed a novel method combining multiscale attention entropy analysis, ICA, and machine learning. Our models achieved better performance than existing ones, with the best MCC, AUC, sensitivity, specificity being 0.545, 0.823, 97.3%, and 97.2%. We also examined the effect of data resolution on model performance and found that the models could achieve good accuracy even when the resolution was as low as 1 minute. This is an important finding because consumer wearables are not able to provide high-resolution data as clinical devices do, and being able to achieve good performance at low data resolution means our models are compatible with consumer devices. Our method is statistically rigid, validated on the largest sleep dataset, and works even for asymptotic patients.Based on the results, we made 6 publications (2 journals, 1 international conference, 2 domestic conference) and gave 1 invited talk at an international conference. We have another 2 journal and 3 conference submissions currently under review.

本研究旨在开发新的SAHS筛选算法，该算法可以使用消费者可穿戴设备收集信号。今年，我们取得了重大突破，获准访问两个大型睡眠数据集，即OSASUD （N=30）和SHHS （N=5768）数据集。我们首先在OSASUD数据集上应用对比集挖掘，发现心率和SpO2与呼吸暂停事件的发生密切相关。然后，我们利用SHHS数据集开发了带有夜间SpO2信号和人口统计信息的呼吸暂停筛查模型。我们提出了一种结合多尺度注意熵分析、ICA和机器学习的新方法。我们的模型性能优于现有模型，最佳MCC、AUC、灵敏度、特异度分别为0.545、0.823、97.3%和97.2%。我们还检查了数据分辨率对模型性能的影响，发现即使分辨率低至1分钟，模型也可以获得良好的准确性。这是一个重要的发现，因为消费者可穿戴设备无法像临床设备那样提供高分辨率数据，并且能够在低数据分辨率下实现良好的性能意味着我们的模型与消费者设备兼容。我们的方法在统计上是严格的，在最大的睡眠数据集上得到了验证，甚至对无症状患者也有效。在此基础上发表论文6篇（期刊2篇，国际会议1次，国内会议2次），在国际会议上发表演讲1次。我们目前正在审查另外两份期刊和三份会议意见书。

项目成果

Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers

利用来自消费者活动跟踪器的日内步数和心率时间序列数据进行情景感知睡眠分析

• DOI: 10.5220/0010892900003123
• 发表时间: 2022
• 期刊: Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
• 作者: Liang Zilu;Nhung Huyen;Bertrand Lauriane;Cleyet-Marrel Nathan
• 通讯作者: Cleyet-Marrel Nathan

What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep

睡眠中的大脑告诉我们什么关于压力的信息？

• DOI: 10.3389/fcomp.2021.774949
• 发表时间: 2021
• 期刊: Frontiers in Computer Science
• 影响因子: 2.6
• 作者: Liang Z;Chapa-Martell MA;Liang Zilu
• 通讯作者: Liang Zilu

Self-rated sleep quality is associated with hemodynamics in prefrontal cortex in the first sleep cycle

自评睡眠质量与第一个睡眠周期中前额皮质的血流动力学相关

• 发表时间: 2021
• 作者: 後藤 和彦;杉 剛直;池田 拓郎;山崎 貴男;飛松 省三;後藤 純信;Liang Z
• 通讯作者: Liang Z

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