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Recent Advances in Seismocardiography

1
Department of Biomedical Engineering, University of California Davis, One Shields Ave, Davis, CA 95616, USA
2
Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
3
College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA
*
Author to whom correspondence should be addressed.
Vibration 2019, 2(1), 64-86; https://doi.org/10.3390/vibration2010005
Received: 29 August 2018 / Revised: 1 December 2018 / Accepted: 4 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Heart Vibrations: An Emerging Cardiovascular Diagnostic Method)
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PDF [1409 KB, uploaded 14 March 2019]
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Abstract

Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG. View Full-Text
Keywords: seismocardiography; heart-induced vibrations; cardiovascular disease; signal processing; signal segmentation; noise removal; feature extraction; machine learning seismocardiography; heart-induced vibrations; cardiovascular disease; signal processing; signal segmentation; noise removal; feature extraction; machine learning
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Taebi, A.; Solar, B.E.; Bomar, A.J.; Sandler, R.H.; Mansy, H.A. Recent Advances in Seismocardiography. Vibration 2019, 2, 64-86.

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