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In addition, factors such as poor superficial blood perfusion and skin color affect the quality of the obtained signal and consequently, the accuracy of beat-to-beat heart rate 11, 12. The most significant limitation of OHR technology is its high sensitivity to movement artefacts, which poses challenges for the signal processing algorithms to choose only those heartbeats or heartbeat intervals that are not affected by movements.
Further, studies evaluating the applicability of wrist-worn OHR technology in estimating HRV in hospitalised patients have been scantly reported.Ī recent study reported poor performance of HRV estimation with commercial wrist OHR device in uncontrolled conditions 10 highlighting the need for improvements in the measurement technology or signal analysis methods. Studies evaluating the performance in beat-to-beat heart rate monitoring and accuracy of HRV parameters have usually been performed in controlled situations during selected activities or at rest as in 7, 8 or as reviewed in 9. Besides arrhythmias, the performance of wrist-worn OHR monitoring has also 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 subjects 5, 6. In the absence of motion, the OHR technique is also able to estimate individual beat-to-beat intervals (BBIs) relatively accurately and has therefore recently emerged as an unobtrusive method for detecting cardiac arrhythmias 1, 2, 3. Reflective photoplethysmography (PPG) measured with a wrist-worn device, also called optical heart rate (OHR) monitoring, is a technique traditionally used mainly in the wellness application domain for monitoring heart rate level during exercise. However, in order to be usable in practice, the data used by the automatic analysis algorithms needs to be reliable and accurate. 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. 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. 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. 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. 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. HRV analysis has also potential in monitoring the recovery of patients, e.g.
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. 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.
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