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

• Published in: IEEE journal of biomedical and health informatics 2019-11, Vol.23 (6), p.2365-2374
• Main Authors: Shandhi, Md Mobashir Hasan, Semiz, Beren, Hersek, Sinan, Goller, Nazli, Ayazi, Farrokh, Inan, Omer T.
• Format: Article
• Language: English
• Subjects: Accelerometer; Accelerometers; Accelerometry - instrumentation; Adult; Algorithms; Angular velocity; Biomedical measurement; cardiovascular monitoring; Ejection; Electrocardiography; ensemble regression; Estimation; Feature extraction; Female; gyroscope; Gyroscopes; Heart - physiology; Heart failure; Heart Function Tests - instrumentation; Heart Function Tests - methods; Humans; Intervals; Male; Monitoring, Physiologic; Morphology; Muscle contraction; Regression analysis; Root-mean-square errors; seismocardiogram; Seismocardiography; sensor fusion; Sensors; Signal Processing, Computer-Assisted - instrumentation; Sternum; Time domain analysis; Wearable Electronic Devices; wearable sensors; Wearable technology; Young Adult
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2895775

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.