WEKO3
アイテム
Heat illness detection with heart rate variability analysis and anomaly detection algorithm
http://hdl.handle.net/10061/0002000591
http://hdl.handle.net/10061/00020005912b516c35-044f-4c9c-b166-cd76a0232b53
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||||||||
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| 公開日 | 2024-10-17 | |||||||||||||||||||
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| タイトル | Heat illness detection with heart rate variability analysis and anomaly detection algorithm | |||||||||||||||||||
| 言語 | ||||||||||||||||||||
| 言語 | eng | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Heatstroke | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Anomaly detection | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Heart rate variability analysis | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Multivariate statistical process control | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Wearable sensor | |||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||
| アクセス権 | ||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||
| 著者 |
Fujiwara, Koichi
× Fujiwara, Koichi
× Ota, Koshi
× Saeda, Shota
× Yamakawa, Toshitaka
× 久保, 孝富× Yamamoto, Aozora
× Maruno, Yuki
× Kano, Manabu
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| 抄録 | ||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | 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. |
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| 書誌情報 |
en : Biomedical Signal Processing and Control 巻 87, 発行日 2023-10-03 |
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| 出版者 | ||||||||||||||||||||
| 出版者 | Elsevier | |||||||||||||||||||
| ISSN | ||||||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||||||
| 収録物識別子 | 1746-8108 | |||||||||||||||||||
| 出版者版DOI | ||||||||||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | https://doi.org/10.1016/j.bspc.2023.105520 | |||||||||||||||||||
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| 関連タイプ | isReplacedBy | |||||||||||||||||||
| 識別子タイプ | URI | |||||||||||||||||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S1746809423009539 | |||||||||||||||||||
| 権利 | ||||||||||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||||||||
| 権利情報 | $00A9 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). | |||||||||||||||||||
| 著者版フラグ | ||||||||||||||||||||
| 出版タイプ | NA | |||||||||||||||||||
