Heat illness detection with heart rate variability analysis and anomaly detection algorithm

被引:3
作者
Fujiwara, Koichi [1 ]
Ota, Koshi [1 ]
Saeda, Shota [1 ]
Yamakawa, Toshitaka [2 ]
Kubo, Takatomi [3 ]
Yamamoto, Aozora [4 ]
Maruno, Yuki [4 ]
Kano, Manabu [5 ]
机构
[1] Nagoya Univ, Dept Mat Proc Engn, Fro Cho,Chikusa Ku, Nagoya, Aichi 4648601, Japan
[2] Kumamoto Univ, Fac Adv Sci & Technol, Kumamoto 8600862, Japan
[3] Nara Inst Sci & Technol, Fac Adv Sci & Technol, Ikoma, Nara 6300101, Japan
[4] Kyoto Womens Univ, Dept Contemporary Soc, Kyoto 6058501, Japan
[5] Kyoto Univ, Dept Syst Sci, Kyoto 6058501, Japan
关键词
Heatstroke; Anomaly detection; Heart rate variability analysis; Multivariate statistical process control; Wearable sensor; STATISTICAL PROCESS-CONTROL; PRINCIPAL COMPONENT ANALYSIS; TEMPERATURE; MANAGEMENT;
D O I
10.1016/j.bspc.2023.105520
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective: Incidence of heat illness has been increasing dramatically due to the progression of global warming. Preventing severe heat illness, called heatstroke, is crucial because it can lead to long-term multiple organ damage, including the brain, and results in more than 600 deaths per year in the United States. It has been reported that heat stress affects heart rate variability (HRV), which is the fluctuations of the R-R interval (RRI) on an electrocardiogram (ECG). We propose a method for detecting symptoms of heat illness based on HRV analysis in order to prevent exacerbation of heat illness. Methods: In the proposed method, monitoring abnormal changes in HRV caused by heat stress is monitored. Multivariate statistical process control (MSPC), a commonly used anomaly detection method in machine learning, is adopted for training the heat illness detection method. To validate the proposed method, we recruited 103 healthy volunteers with risks of heat illness development: employees working in hot environments, athletes, and amateur marathon runners. Data collection was performed using our wearable heart rate sensor and smartphone app.Results: The result of applying the proposed method showed that a sensitivity of 75% (21 out of 28 cases) and a false-positive rate of 1.02 times per hour were achieved.Conclusion: The proposed heat illness detection method will be used in daily life because RRI data can be easily measured by a wearable sensor.Significance: The proposed method will contribute to receiving appropriate treatment for heat illness before exacerbation, which contributes to protecting people's health.
引用
收藏
页数:11
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