Abstract
Heart rate variability (HRV), using electrocardiography (ECG), has gained popularity as a biomarker of the stress response. Alternatives to HRV monitoring, like photoplethysmography (PPG), are being explored as cheaper and unobtrusive non-invasive technologies. We report a new wireless PPG sensor that was tested in detecting changes in HRV, elicited by a mentally stressful task, and to determine if its signal can be used as a surrogate of ECG for HRV analysis. Data were collected simultaneously from volunteers using a PPG and ECG sensor, during a resting and a mentally stressful task. HRV metrics were extracted from these signals and compared to determine the agreement between them and to determine if any changes occurred in the metrics due to the stressful task. For both tasks, a moderate/good agreement was found in the mean interbeat intervals, SDNN, LF, and SD2, and a poor agreement for the pNN50, RMSSD|SD1, and HF metrics. The majority of the tested HRV metrics obtained from the PPG signal showed a significant decrease caused by the mental task. The disagreement found between specific HRV features imposes caution when comparing metrics from different technologies. Nevertheless, the tested sensor was successful at detecting changes in the HRV caused by a mental stressor.
Highlights
Heart rate variability (HRV) is the fluctuation over time of consecutive heartbeats and is accepted as a non-invasive biomarker of the activity of the autonomous nervous system [1,2,3]
At rest and during the execution of the SCTW, there was good agreement between the mean intervals, low frequency (LF), and SD2 metrics extracted from the PP intervals provided by the James One and the RR
As for the standard deviation of the intervals (SDNN) metric, there was a good agreement during the SCTW and a moderate agreement at rest
Summary
Heart rate variability (HRV) is the fluctuation over time of consecutive heartbeats and is accepted as a non-invasive biomarker of the activity of the autonomous nervous system [1,2,3]. The analysis of the HRV has been used as a diagnosis and a clinical research tool, since changes in HRV have been associated with several cardiovascular, metabolic, and mental disorders [2,3,4,5]. The ECG signal is considered the gold standard from which the R-peaks from the QRS-complex can be identified using automatic computerized algorithms. The distance between these peaks is used to create time-series of intervals between successive heartbeats (RR intervals) [4,11,12].
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