Key result
LF/HF ratio values derived from wearable ECG sensors using Matlab HRVAS closely correlated with medical ECG recordings, validating consumer-grade biosensors for frequency-based HRV analysis.
Why the study?
The study was conducted to obtain biosignals from photoplethysmograph and electrocardiograph sensors on consumer devices and statistically validate the data for drowsiness estimation.
Do wearable smart HRV sensors provide comparable LF/HF ratio and R-R interval data to medical-grade ECG equipment in healthy volunteers?
Observational (n=18)
Do wearable smart HRV sensors provide comparable LF/HF ratio and R-R interval data to medical-grade ECG equipment in healthy volunteers?
Consumer-grade wearable ECG sensors like the Polar H7 can provide valid R-R interval data for frequency-based HRV analysis comparable to medical-grade ECG equipment.
Consumer device HRV signals may enable drowsiness estimation; leaves open real-world validation before any clinical use.
The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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Markovičs et al. (2018) conducted an observational in Healthy (n=18). Wearable smart HRV sensors (Polar H7) and Matlab HRVAS vs. Medical grade ECG (Welch Allyn Cardio Perfect Pro) was evaluated on Correlation of LF/HF ratio and R-R intervals. LF/HF ratio values derived from wearable ECG sensors using Matlab HRVAS closely correlated with medical ECG recordings, validating consumer-grade biosensors for frequency-based HRV analysis.
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