Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation

Overview

Journal IEEE J Biomed Health Inform

Specialties Biomedical Engineering
Medical Informatics

Date 2019 Feb 1

PMID 30703050

Citations 24

Authors

Md Mobashir Hasan Shandhi

Beren Semiz

Sinan Hersek

Nazli Goller

Farrokh Ayazi

Omer T Inan

Affiliations

Soon will be listed here.

Abstract

Objective: Systolic time intervals, such as the pre-ejection period (PEP), are important parameters for assessing cardiac contractility that can be measured non-invasively using seismocardiography (SCG). Recent studies have shown that specific points on accelerometer- and gyroscope-based SCG signals can be used for PEP estimation. However, the complex morphology and inter-subject variation of the SCG signal can make this assumption very challenging and increase the root mean squared error (RMSE) when these techniques are used to develop a global model.

Methods: In this study, we compared gyroscope- and accelerometer-based SCG signals, individually and in combination, for estimating PEP to show the efficacy of these sensors in capturing valuable information regarding cardiovascular health. We extracted general time-domain features from all the axes of these sensors and developed global models using various regression techniques.

Results: In single-axis comparison of gyroscope and accelerometer, angular velocity signal around head to foot axis from the gyroscope provided the lowest RMSE of 12.63 ± 0.49 ms across all subjects. The best estimate of PEP, with a RMSE of 11.46 ± 0.32 ms across all subjects, was achieved by combining features from the gyroscope and accelerometer. Our global model showed 30% lower RMSE when compared to algorithms used in recent literature.

Conclusion: Gyroscopes can provide better PEP estimation compared to accelerometers located on the mid-sternum. Global PEP estimation models can be improved by combining general time domain features from both sensors.

Significance: This work can be used to develop a low-cost wearable heart-monitoring device and to generate a universal estimation model for systolic time intervals using a single- or multiple-sensor fusion.

Citing Articles

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Skoric J, DMello Y, Plant D IEEE J Transl Eng Health Med. 2024; 12:348-358.

PMID: 38606390 PMC: 11008810. DOI: 10.1109/JTEHM.2024.3368291.

Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals.

Soliman M, Ganti V, Inan O IEEE Sens J. 2023; 22(18):18093-18103.

PMID: 37091042 PMC: 10120872. DOI: 10.1109/jsen.2022.3196601.

Synthetic seismocardiogram generation using a transformer-based neural network.

Nikbakht M, Gazi A, Zia J, An S, Lin D, Inan O J Am Med Inform Assoc. 2023; 30(7):1266-1273.

PMID: 37053380 PMC: 10280352. DOI: 10.1093/jamia/ocad067.

Wearable Devices in Cardiovascular Medicine.

Hughes A, Shandhi M, Master H, Dunn J, Brittain E Circ Res. 2023; 132(5):652-670.

PMID: 36862812 PMC: 9991078. DOI: 10.1161/CIRCRESAHA.122.322389.

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement.

Yuan B, Tang Z, Zhang P, Lv F Sensors (Basel). 2023; 23(4).

PMID: 36850700 PMC: 9964833. DOI: 10.3390/s23042105.

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