Jianbo Xu1, Wenxi Chen1. 1. Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan.
Abstract
BACKGROUND: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. METHODS: The bathtub WT was preset at three conditions: i.e., low WT (36-38 °C), medium WT (38-40 °C), and high WT (40-42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of t-tests, and the mean and standard deviation (SD) of each HRV feature based on the WT were calculated. RESULTS: The statistics show that with increasing WT, 11 HRV features are significantly (p < 0.05) and monotonously reduced, four HRV features are significantly (p < 0.05) and monotonously rising, two HRV features are rising first and then reduced, two HRV features (fuzzy and approximate entropy) are almost unchanged, and vLF power is rising. CONCLUSION: The WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works.
BACKGROUND: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. METHODS: The bathtub WT was preset at three conditions: i.e., low WT (36-38 °C), medium WT (38-40 °C), and high WT (40-42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of t-tests, and the mean and standard deviation (SD) of each HRV feature based on the WT were calculated. RESULTS: The statistics show that with increasing WT, 11 HRV features are significantly (p < 0.05) and monotonously reduced, four HRV features are significantly (p < 0.05) and monotonously rising, two HRV features are rising first and then reduced, two HRV features (fuzzy and approximate entropy) are almost unchanged, and vLF power is rising. CONCLUSION: The WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works.
Entities:
Keywords:
ECG; bathing; heart rate variability; quantitative analysis; t-test; water temperature
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