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Long-Term Tracking of a Patient’s Health Condition Based on Pulse Rate Dynamics During Sleep

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Abstract

This article proposed a method to track the changes in health condition of a patient after coronary stenting over seven successive seasons based on daily pulse rate (PR). The pulse signal was recorded by an unconstrained monitoring system during sleep. Seasonal PR dynamics were evaluated by both linear measures, including time domain and frequency domain indexes, and nonlinear measures such as noise limit (NL), detection rate (DR), sample entropy (SampEn), and Poincaré plots. NL and DR were derived using the noise titration method. Significant changes in seasonal indexes of the patient were evaluated statistically. The results show that an overall downward trend of the PR dynamics corresponds to changes in the patient’s health condition that began in winter and developed in spring and worsened most seriously in the following summer. The monthly and seasonal orbits of PR nonlinearity of the patient were plotted and observed to follow different trajectory compared with a healthy subject. These results indicate the feasibility of applying dynamics of PR as a potential prognostic tool for detecting early changes in a patient’s health condition and also for understanding the temporal transition of health condition over a long-term period.

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Abbreviations

AECG:

Ambulatory electrocardiogram

ANS:

Autonomic nervous system

DR:

Detection rate

HF:

High frequency

HRV:

Heart rate variability

LF:

Low frequency

NL:

Noise limit

NT:

Noise titration

PI:

Pulse interval

PRV:

Pulse rate variability

PSD:

Power spectral density

SampEn:

Sample entropy

SDNN:

The mean of the 5-min standard deviation of the NN (normal RR) intervals over 24 h

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Acknowledgments

The authors would like to thank the volunteers for their endurance in daily data collection and colleagues in the research project. Authors would like also to thank Prof. Chi-Sang Poon for sharing with the source code of noise titration method.

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Authors and Affiliations

  1. Biomedical Information Technology Laboratory, The University of Aizu, Ikki-machi, Aizu-Wakamatsu, Fukushima, 965-8580, Japan

    Ying Chen & Wenxi Chen

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  1. Ying Chen
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Correspondence to Wenxi Chen.

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Associate Editor Ioannis A. Kakadiaris oversaw the review of this article.

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Chen, Y., Chen, W. Long-Term Tracking of a Patient’s Health Condition Based on Pulse Rate Dynamics During Sleep. Ann Biomed Eng 39, 2922–2934 (2011). https://doi.org/10.1007/s10439-011-0397-z

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  • DOI: https://doi.org/10.1007/s10439-011-0397-z

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