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

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.