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Effect of Respiration and Exercise on Seismocardiographic Signals

Overview
Journal Comput Biol Med
Publisher Elsevier
Date 2024 Dec 22
PMID 39709866
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Abstract

Background: Seismocardiographic signals (SCG) are chest wall vibrations induced by mechanical cardiac activities. This study investigated the morphological changes in the SCG signal due to respiration and exercise.

Methods: Fifteen healthy subjects were recruited, and SCG was acquired before and after exercise. The changes in the SCG signal were quantified using time and amplitude features.

Results: The amplitudes of the two main SCG events (SCG1 and SCG2) tended to increase after exercise. The absolute cardiac intervals (pre-ejection period (PEP), left ventricular ejection time (LVET), and diastolic time) decreased; the diastolic time relative to cardiac cycle duration (i.e., the R-R interval) also decreased, while the relative PEP and LVET increased for the majority of the subjects. Amplitude modulations were observed in both SCG1 and SCG2 and increased with exercise. Additionally, respiratory influences on the SCG features were observed in both the pre- and post-exercise states. SCG2 amplitude was higher during inspiration (p < 0.01), but SCG1 amplitude didn't exhibit consistent changes with respiration in the study subjects (p > 0.05). For cardiac intervals, PEP decreased during inspiration, while LVET and diastolic time increased (p < 0.01). All the cardiac intervals (both absolute and as a percentage of cardiac cycle duration) showed reduced respiratory variability post-exercise.

Conclusions: These results document SCG signal variabilities that were not reported before and provide a link between cardiac activity, respiration, and exercise, which may help increase the clinical utility of SCG in the diagnosis and management of cardiopulmonary conditions. More studies are required to validate the study findings in more normal subjects and in those with cardiopulmonary pathology.

References
1.
Khosrow-Khavar F, Tavakolian K, Blaber A, Menon C . Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals. IEEE Trans Biomed Eng. 2017; 64(8):1701-1710. DOI: 10.1109/TBME.2016.2616382. View

2.
Azad M, Gamage P, Dhar R, Sandler R, Mansy H . Postural and longitudinal variability in seismocardiographic signals. Physiol Meas. 2023; 44(2). PMC: 9969814. DOI: 10.1088/1361-6579/acb30e. View

3.
El-Segaier M, Lilja O, Lukkarinen S, Sornmo L, Sepponen R, Pesonen E . Computer-based detection and analysis of heart sound and murmur. Ann Biomed Eng. 2005; 33(7):937-42. DOI: 10.1007/s10439-005-4053-3. View

4.
Castiglioni P, Faini A, Parati G, Di Rienzo M . Wearable seismocardiography. Annu Int Conf IEEE Eng Med Biol Soc. 2007; 2007:3954-7. DOI: 10.1109/IEMBS.2007.4353199. View

5.
Shaw L, Mieres J, Hendel R, Boden W, Gulati M, Veledar E . Comparative effectiveness of exercise electrocardiography with or without myocardial perfusion single photon emission computed tomography in women with suspected coronary artery disease: results from the What Is the Optimal Method for Ischemia.... Circulation. 2011; 124(11):1239-49. DOI: 10.1161/CIRCULATIONAHA.111.029660. View