Key result
A proposed seismocardiogram system using adaptive filtering estimated a heart rate of 65 bpm and detected 27 aortic valve opening peaks with an RMSE of 0.1942 m/s2 in a noisy signal.
Why the study?
The study evaluated whether seismocardiogram measurements could detect drivers' heart problems before reaching a critical condition to help safely stop vehicles in a nonclinical manner.
A proposed seismocardiogram system using MEMS sensors and adaptive filtering can estimate heart rate and detect aortic valve opening peaks in noisy environments, demonstrating technical feasibility for nonclinical driver cardiovascular monitoring.
Seismocardiogram monitoring may aid road accident prevention; leaves open real-world validation in prospective studies.
This article deals with the treatment and application of cardiac biosignals, an excited accelerometer, and a gyroscope in the prevention of accidents on the road. Previously conducted studies say that the seismocardiogram is a measure of cardiac microvibration signals that allows for detecting rhythms, heart valve opening and closing disorders, and monitoring of patients’ breathing. This article refers to the seismocardiogram hypothesis that the measurements of a seismocardiogram could be used to identify drivers’ heart problems before they reach a critical condition and safely stop the vehicle by informing the relevant departments in a nonclinical manner. The proposed system works without an electrocardiogram, which helps to detect heart rhythms more easily. The estimation of the heart rate (HR) is calculated through automatically detected aortic valve opening (AO) peaks. The system is composed of two micro-electromechanical systems (MEMSs) to evaluate physiological parameters and eliminate the effects of external interference on the entire system. The few digital filtering methods are discussed and benchmarked to increase seismocardiogram efficiency. As a result, the fourth adaptive filter obtains the estimated HR = 65 beats per min (bmp) in a still noisy signal (SNR = −11.32 dB). In contrast with the low processing benefit (3.39 dB), 27 AO peaks were detected with a 917.56-ms peak interval mean over 1.11 s, and the calculated root mean square error (RMSE) was 0.1942 m/s2 when the adaptive filter order is 50 and the adaptation step is equal to 0.933.
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Uskovas et al. (2022) studied Cardiovascular disease detection in drivers. Seismocardiogram (SCG) system using MEMS sensors was evaluated on Heart rate estimation and aortic valve opening peak detection. A proposed seismocardiogram system using adaptive filtering estimated a heart rate of 65 bpm and detected 27 aortic valve opening peaks with an RMSE of 0.1942 m/s2 in a noisy signal.
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