Recent Advances in Seismocardiography

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.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

“…For the latter, it has been shown that the morphology of the vibrational cardiac waveforms varies with the respiratory volume [39,40], and methods of extracting respiratory phases [41,42], effort [43] and breathing states such as normal, breathless, long, and labored [44] have been proposed. However, the interindividual morphological variations, as well as dependencies on numerous parameters including subject position, are still challenging [45]. SCG and PCG are closely related [46], with differences mostly defined by their frequency ranges [47].…”

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

“…All the parameters are defined in Equations (1)-(4) where ( ) d n is the desired signal. Sensors 2020, 20, x 3 of 14 ( ) ( ) ( ) n d n y n    (4) The adaptive control unit updates the coefficients using the input vector and the prior estimation error. The detailed information can be described in Equation (5): (5) where ( ) n k is the gain vector that is described in Equation (6): (6) where  is the forgetting factor and ( ) n P is a covariance matrix of the noise which can be updated by Equation (7):…”

“…With the development of an accelerometer with high-sensitivity, low-noise, small-size, high-efficiency, and high-robustness signal-processing technology, SCG has shown its great potential to be used by wearables. Consequently, it is now feasible to use the information in clinical applications [3,4].…”

“…Motion artifact is usually irregular and it is mixed with the heartbeat signals in the time and frequency domains. The mixture makes it difficult to separate the heartbeat signal from the mixed signal [3][4][5]. Some researchers tried to use multiple sensors to remove the motion artifact from the recorded signals [6][7][8][9].…”

Precision wearable accelerometer contact microphones for longitudinal monitoring of mechano-acoustic cardiopulmonary signals

“…20,21 In addition, micro-vibrations of the chest wall in the low frequency range of DC-100 Hz are linked to mechanical cardiac events and contain information about valve functionalities and cardiac muscle contractility. 22,23 Moreover, changes in the lung volume during the respiratory cycle cause macro-motions of the thorax and abdomen with ultralow frequency below 1 Hz. 24 This signal is useful to extract the respiratory pattern, which is significant in predicting clinical deterioration of patients with chronic airflow obstruction, 25,26 and diagnose several other respiratory diseases.…”