New ubiquitous computing system uniting chaos theory and data science for sleep apnea hypopnea syndrome (SAHS) screening with wearable devices
New ubiquitous computing system uniting chaos theory and data science for sleep apnea hypopnea syndrome (SAHS) screening with wearable devices
批准号:
21K17670
负责人:
梁 滋路
金额:
$3.0万
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
项目状态:
未结题
起止时间:
2021-04-01 至 2025-03-31
中文摘要
这项研究的目的是开发新的算法,用于SAHS筛查,可以使用消费者可穿戴设备收集信号。我们今年取得了重大突破,因为我们获得了访问两个大型睡眠数据集的批准,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数据集开发具有夜间SpO2信号和人口统计信息的呼吸暂停筛查模型。我们提出了一种新的方法相结合的多尺度注意熵分析,伊卡和机器学习。我们的模型比现有模型具有更好的性能,最佳MCC,AUC,灵敏度,特异性分别为0.545,0.823,97.3%和97.2%。我们还研究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,模型也可以达到良好的准确性。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,而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。我们的方法在统计学上是严格的,在最大的睡眠数据集上得到了验证,甚至对渐近患者也有效。基于结果,我们发表了6篇论文(2篇期刊,1篇国际会议,2篇国内会议),并在国际会议上发表了1次特邀演讲。我们还有另外2个期刊和3个会议提交目前正在审查中。
英文摘要
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.
期刊论文(28)
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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
影响因子:
--
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
Research Gate Profile
研究门简介
DOI:
--
发表时间:
期刊:
影响因子:
--
作者:
[]
通讯作者:
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
DOI:
--
发表时间:
2021
期刊:
影响因子:
--
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
Lab homepage
实验室主页
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