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Postural and longitudinal variability in seismocardiographic signals

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence...

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Autores principales: Azad, Md Khurshidul, Gamage, Peshala T, Dhar, Rajkumar, Sandler, Richard H, Mansy, Hansen A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969814/
https://www.ncbi.nlm.nih.gov/pubmed/36638534
http://dx.doi.org/10.1088/1361-6579/acb30e
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author Azad, Md Khurshidul
Gamage, Peshala T
Dhar, Rajkumar
Sandler, Richard H
Mansy, Hansen A
author_facet Azad, Md Khurshidul
Gamage, Peshala T
Dhar, Rajkumar
Sandler, Richard H
Mansy, Hansen A
author_sort Azad, Md Khurshidul
collection PubMed
description Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing. Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally. Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5–50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different, Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.
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spelling pubmed-99698142023-02-28 Postural and longitudinal variability in seismocardiographic signals Azad, Md Khurshidul Gamage, Peshala T Dhar, Rajkumar Sandler, Richard H Mansy, Hansen A Physiol Meas Paper Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing. Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally. Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5–50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different, Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time. IOP Publishing 2023-02-01 2023-02-27 /pmc/articles/PMC9969814/ /pubmed/36638534 http://dx.doi.org/10.1088/1361-6579/acb30e Text en © 2023 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Azad, Md Khurshidul
Gamage, Peshala T
Dhar, Rajkumar
Sandler, Richard H
Mansy, Hansen A
Postural and longitudinal variability in seismocardiographic signals
title Postural and longitudinal variability in seismocardiographic signals
title_full Postural and longitudinal variability in seismocardiographic signals
title_fullStr Postural and longitudinal variability in seismocardiographic signals
title_full_unstemmed Postural and longitudinal variability in seismocardiographic signals
title_short Postural and longitudinal variability in seismocardiographic signals
title_sort postural and longitudinal variability in seismocardiographic signals
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969814/
https://www.ncbi.nlm.nih.gov/pubmed/36638534
http://dx.doi.org/10.1088/1361-6579/acb30e
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