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

Shandhi, Md. Mobashir Hasan; Semiz, Beren; Hersek, Sinan; Goller, Nazli; Ayazi, Farrokh

doi:10.1109/jbhi.2019.2895775

Cited by 51 publications

(26 citation statements)

References 31 publications

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“…Such types of sensors cannot capture both mechanical and acoustic aspects of MA signals simultaneously, with high fidelity. Compact, skin-mounted accelerometers and/or gyroscopes based on microelectromechanical systems (MEMS) offer important capabilities in this context, with demonstrated examples in capturing signatures of cardiac mechanics such as seismocardiograms or ballistocardiograms [11][12][13][14] , respiratory rate and sounds [15][16][17][18][19][20][21] , sounds of swallowing [22][23][24][25] , vocals 26,27 , changes in body position and motion [28][29][30] and others 31 . Certain of these measurements have direct relevance to medical applications, particularly when implemented in devices that monitor multiple physiological signals 1,3,32 .…”

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confidence: 99%

Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch

et al. 2019

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Skin-mounted soft electronics incorporating high-bandwidth triaxial accelerometers can provide broad classes of physiologically relevant information, such as mechanoacoustic signatures of underlying body processes (such as those captured by a stethoscope) and precision kinematics of core body motions. Here, we describe a wireless device designed to be conformally placed on the suprasternal notch for the continuous measurement of mechanoacoustic signals, from subtle vibrations of the skin at accelerations of ~10 −3 m•s −2 to large motions of the entire body at ~10 m•s −2 , and at frequencies up to ~800 Hz. Because th measurements are a complex superposition of signals that arise from locomotion, body orientation, swallowing, respiration, cardiac activity, vocal-fold vibrations and other sources, we used frequency-domain analysis and machine learning to obtain, from human subjects during natural daily activities and exercise, real-time recordings of heart rate, respiration rate, energy intensity and other essential vital signs, as well as talking time and cadence, swallow counts and patterns, and other unconventional biomarkers. We also used the device in sleep laboratories, and validated the measurements via polysomnography. Natural processes of the human body yield a multitude of mechano-acoustic (MA) signals, many of which strongly attenuate at the skin-air interface 1-5. Motions with amplitudes and Lee et al.

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confidence: 99%

Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch

et al. 2019

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“…In a recent study based on the regression, the PEP was estimated using the features from the 3-axial SCG and GCG [4] and was compared with the same parameter estimated from ICG as a reference. Our results are comparable with the reported results of that study.…”

Section: Discussionmentioning

confidence: 99%

“…For each axis of the SCG and GCG signals, 18 features were extracted per cardiac cycle [4]. Twelve features, associated with systole, were extracted from the first part of each cycle, from ECG Q to ECG Q + 150 ms and six features, related to diastole, were extracted from the time interval between ECG Q + 250 ms and ECG Q + 450 ms.…”

Section: Feature Extractionmentioning

confidence: 99%

Estimation of Cardiac Time Intervals from the Mechanical Activity of the Heart Using Machine Learning

Dehkordi¹,

Tavakolian²,

Zhao³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Pre-ejection period (PEP) and total systolic time (TST) are hemodynamic indices which provide vital information about left ventricular performance. In this study, we suggested a method for estimating PEP and TST using the features extracted from the mechanical activity of the heart without finding the fiducial points associated with the opening and closure of heart valves. We recorded seismocardiogram (SCG) and gyrocardiogram (GCG) signals from 50 healthy subjects using a 3-axial micro-electromechanical joint accelerometer-gyroscope sensor. For each axis of the SCG and GCG signals, 18 features were extracted per cardiac cycle. Several regression models were implemented using the features from multiple combinations of SCG and GCG and were validated against the measurements from tissue Doppler imaging (TDI) as the reference. The model that was fitted over the SCG z-axis features provided the most accurate estimates for PEP and TST intervals with the root mean square error of 9.5 and 17.2 millisecond, respectively. The estimation of cardiac time intervals investigated in this study broadens the potential of SCG and GCG in the monitoring of cardiovascular performance. As these technologies yield themselves to wearable applications, they could be used outside of hospital/clinical settings to detect abnormalities and malfunctions of the cardiovascular system.

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“…Modern research in BCG and SCG measuring techniques also deals with wearable monitoring systems, allowing continuous vital sign recording during everyday life [ 56 ], in any environment or under stress [ 1 ]. In this case, a three-axis accelerometer is generally used, which can be mechanically attached to the body either by adhesive, plastic fastening or textile [ 79 ]. The SCG assessment using an external three-axis MEMS accelerometer mounted on the left clavicle attached to an intelligent garment with textile ECG electrodes to obtain simultaneous three-axis SCG and single-lead ECG records was thoroughly tested [ 80 ].…”

Section: Overview Of Already Existing Methodsmentioning

confidence: 99%

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

Šidiková

Martínek

Kawala-Sterniuk

et al. 2020

Sensors

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This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

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

Cited by 51 publications

References 31 publications

Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch

Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch

Estimation of Cardiac Time Intervals from the Mechanical Activity of the Heart Using Machine Learning

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

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