Key result
Seismocardiography shows noninvasive monitoring promise, but variability and lack of standardization limit use.
Why the study?
New diagnostic tools are needed for early cardiovascular disease detection and intervention, and recent advances in sensors, signal processing, and machine learning have renewed interest in seismocardiography.
Answers are grounded in this paper and cite their sources.
Seismocardiography, supported by modern signal processing and machine learning, shows promise as a noninvasive tool for continuous bedside or ambulatory cardiac monitoring.
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.
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Taebi et al. (2019) conducted a review in Cardiovascular disease. Seismocardiography was evaluated. Seismocardiography demonstrates potential clinical utility for the noninvasive detection and monitoring of cardiovascular conditions, though clinical application remains limited by signal variability and lack of standardized fiducial points.
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