Seismocardiography has demonstrated potential clinical utility for the noninvasive detection and monitoring of cardiovascular conditions, benefiting from advances in sensors and machine learning.
Seismocardiography is an emerging noninvasive technique for evaluating cardiac activity, with recent advances in sensors and machine learning improving its potential clinical utility.
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.
Taebi et al. (Mon,) conducted a review in Cardiovascular disease. Seismocardiography (SCG) was evaluated. Seismocardiography has demonstrated potential clinical utility for the noninvasive detection and monitoring of cardiovascular conditions, benefiting from advances in sensors and machine learning.