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.

A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control

“…In our earlier investigation (Chen et al, 2008, 2009a), we have compared heartbeat interval fitting point process models using different probability distributions, and found that the inverse Gaussian model achieved the overall best fitting results. In practice, we can always conduct an empirical model fit analysis (e.g., data histogram, the Q-Q plot, and the Kolmogorov-Smirnov plot) for the raw R-R intervals, testing the appropriateness of the inverse Gaussian model (Chen et al, 2011a). …”

“…In light of equation (8), the frequency response for the baroreflex (BP → RR) or RSA (RP → RR) is computed as (Chen et al, 2009a, 2011a)…”

“…Motivated by the importance of quantifying the contribution of non-linearity to HRV and the heartbeat dynamics, we have proposed a quantitative index based on the spectrum-bispectrum ratio (Chen et al, 2010a, 2011a)…”

“…In both cases, the instantaneous ratio is derived as (see Chen et al, 2010a, 2011a) for assumptions and details)…”

“…To provide an exemplary application, we employ the proposed point process methods to analyze experimental recordings from healthy subjects during administration of propofol to induce controlled states of general anesthesia (Purdon et al, 2009). To this extent, as reviewed in this article, our recent investigations have reported promising results in monitoring cardiovascular regulation under induction of anesthesia (Chen et al, 2009b, 2010b, 2011a). …”