Estimation of Circadian Body Temperature Rhythm Based on Heart Rate in Healthy, Ambulatory Subjects

• Published in: IEEE journal of biomedical and health informatics Vol. 21; no. 2; pp. 407 - 415
• Main Authors: Sim, Soo Young, Joo, Kwang Min, Kim, Han Byul, Jang, Seungjin, Kim, Beomoh, Hong, Seungbum, Kim, Sungwan, Park, Kwang Suk
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Amplitudes; Body temperature; Body Temperature - physiology; Circadian rhythm; Circadian Rhythm - physiology; Circadian rhythms; core body temperature; Estimation; Extended Kalman filter; Feasibility studies; Female; Health problems; Heart rate; Heart Rate - physiology; heart rate variability (HRV); Humans; Male; Medical electronics; Mental depression; Monitoring; Monitoring, Physiologic - methods; Plasma temperature; Regression analysis; Rhythm; Signal Processing, Computer-Assisted; Sleep; Sleep disorders; Temperature effects; Temperature measurement; Temperature sensors; Thermometers; Thermoregulation; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2016.2529655

Core body temperature is a reliable marker for circadian rhythm. As characteristics of the circadian body temperature rhythm change during diverse health problems, such as sleep disorder and depression, body temperature monitoring is often used in clinical diagnosis and treatment. However, the use of current thermometers in circadian rhythm monitoring is impractical in daily life. As heart rate is a physiological signal relevant to thermoregulation, we investigated the feasibility of heart rate monitoring in estimating circadian body temperature rhythm. Various heart rate parameters and core body temperature were simultaneously acquired in 21 healthy, ambulatory subjects during their routine life. The performance of regression analysis and the extended Kalman filter on daily body temperature and circadian indicator (mesor, amplitude, and acrophase) estimation were evaluated. For daily body temperature estimation, mean R-R interval (RRI), mean heart rate (MHR), or normalized MHR provided a mean root mean square error of approximately 0.40 °C in both techniques. The mesor estimation regression analysis showed better performance than the extended Kalman filter. However, the extended Kalman filter, combined with RRI or MHR, provided better accuracy in terms of amplitude and acrophase estimation. We suggest that this noninvasive and convenient method for estimating the circadian body temperature rhythm could reduce discomfort during body temperature monitoring in daily life. This, in turn, could facilitate more clinical studies based on circadian body temperature rhythm.