Abstract
Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat-to-beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non-healthy subjects. In this paper, we present a method to estimate beat-to-beat-intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross-sectional study with 29 hospitalized patients (mean age 70.6 years) in 24-h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high-frequency content of the HRV and especially the LF/HF-ratio should be used with caution.
Highlights
Unobtrusive continuous monitoring and automatic analysis of physiological variables is an emerging area that has the potential to improve the effectiveness of healthcare delivery by providing early indications in the changes of the patients’ status, whether being treated in a hospital or staying at home
The performance of wrist-worn optical heart rate (OHR) monitoring has been studied, for example, in the assessment of psychological stress[4] and in sleep staging through heart rate variability (HRV) and movement analysis in healthy s ubjects[5,6]
The accuracy of HRV parameters estimated from the PPG signal of wrist-worn OHR monitoring device varies significantly between parameters and subjects
Summary
Unobtrusive continuous monitoring and automatic analysis of physiological variables is an emerging area that has the potential to improve the effectiveness of healthcare delivery by providing early indications in the changes of the patients’ status, whether being treated in a hospital or staying at home. SDNN and HRV triangular index measured on post-operative day 1 were found to be statistically significantly lower in digestive surgery patients developing post-operative c omplications[14] Both Nenna et al as well as Huikuri and Stein identified scaling exponent α1 of detrended fluctuation analysis as a non-linear HRV parameter with high prognostic value in predicting long-term cardiac mortality[16,17]. This parameter was found to be the best predictor of complicated recovery after coronary artery bypass g rafting[21]. In the studies that have evaluated the heart rate level as a potential indicator, increased post-operative heart rate has been found to predict or be associated with post-operative complications
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