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  • Title: Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation.
    Author: Shandhi MMH, Semiz B, Hersek S, Goller N, Ayazi F, Inan OT.
    Journal: IEEE J Biomed Health Inform; 2019 Nov; 23(6):2365-2374. PubMed ID: 30703050.
    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.
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