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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
IOP Publishing
2023
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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. |
format | Online Article Text |
id | pubmed-9969814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | IOP Publishing |
record_format | MEDLINE/PubMed |
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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