Key result
ECG-based wearable smart sensors correlated with medical ECG equipment for frequency-based HRV analysis, whereas optical PPG sensors did not provide adequate frequency-domain results.
Why the study?
The study was conducted to obtain biosignals from photoplethysmograph and electrocardiograph sensors on selected consumer devices and statistically validate the data for use with a drowsiness estimation method.
Do consumer-grade wearable smart sensors provide valid HRV signals (LF/HF ratio) compared to certified medical ECG equipment for drowsiness estimation?
Observational
Do consumer-grade wearable smart sensors provide valid HRV signals (LF/HF ratio) compared to certified medical ECG equipment for drowsiness estimation?
Consumer-grade ECG-based wearable biosensors can provide valid R-R interval data for frequency-based HRV analysis compared to certified medical equipment, though optical sensors may be less reliable for this specific application.
Consumer device HRV data may aid drowsiness monitoring; leaves open rigorous clinical validation before practice adoption.
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) reported an observational. Wearable smart HRV sensors (ECG and PPG) vs. Medical ECG equipment was evaluated on LF/HF ratio correlation between wearable sensors and medical ECG equipment. ECG-based wearable smart sensors correlated with medical ECG equipment for frequency-based HRV analysis, whereas optical PPG sensors did not provide adequate frequency-domain results.
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