Heart-rate monitoring by air pressure and causal analysis

Naoki Tsuchiya; Hiroshi Nakajima; Yutaka Hata

doi:10.1117/12.883731

3 June 2011 Heart-rate monitoring by air pressure and causal analysis

Naoki Tsuchiya, Hiroshi Nakajima, Yutaka Hata

Proceedings Volume 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX; 805811 (2011) https://doi.org/10.1117/12.883731
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Among lots of vital signals, heart-rate (HR) is an important index for diagnose human's health condition. For instance, HR provides an early stage of cardiac disease, autonomic nerve behavior, and so forth. However, currently, HR is measured only in medical checkups and clinical diagnosis during the rested state by using electrocardiograph (ECG). Thus, some serious cardiac events in daily life could be lost. Therefore, a continuous HR monitoring during 24 hours is desired. Considering the use in daily life, the monitoring should be noninvasive and low intrusive. Thus, in this paper, an HR monitoring in sleep by using air pressure sensors is proposed. The HR monitoring is realized by employing the causal analysis among air pressure and HR. The causality is described by employing fuzzy logic. According to the experiment on 7 males at age 22-25 (23 on average), the correlation coefficient against ECG is 0.73-0.97 (0.85 on average). In addition, the cause-effect structure for HR monitoring is arranged by employing causal decomposition, and the arranged causality is applied to HR monitoring in a setting posture. According to the additional experiment on 6 males, the correlation coefficient is 0.66-0.86 (0.76 on average). Therefore, the proposed method is suggested to have enough accuracy and robustness for some daily use cases.

Citation Download Citation

Naoki Tsuchiya, Hiroshi Nakajima, and Yutaka Hata "Heart-rate monitoring by air pressure and causal analysis", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805811 (3 June 2011); https://doi.org/10.1117/12.883731

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