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Area asymmetry of heart rate variability signal
BACKGROUND: Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series. METHOD: An area index (AI) was developed that combines the distance and phase angle information of point...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607847/ https://www.ncbi.nlm.nih.gov/pubmed/28934961 http://dx.doi.org/10.1186/s12938-017-0402-3 |
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author | Yan, Chang Li, Peng Ji, Lizhen Yao, Lianke Karmakar, Chandan Liu, Changchun |
author_facet | Yan, Chang Li, Peng Ji, Lizhen Yao, Lianke Karmakar, Chandan Liu, Changchun |
author_sort | Yan, Chang |
collection | PubMed |
description | BACKGROUND: Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series. METHOD: An area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta’s index (PI), Guzik’s index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from >3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG. RESULTS: For long-term data, SI showed acceptable performance for both tasks, i.e., for task i p < 0.001, Cohen’s d = 0.93, AUC (area under the receiver-operating characteristic curve) = 0.86; for task ii p < 0.001, d = 0.88, AUC = 0.75. AI performed well for task ii (p < 0.001, d = 1.0, AUC = 0.78); for task i, though the difference was statistically significant (p < 0.001, AUC = 0.76), the effect size was small (d = 0.11). PI and GI failed in both tasks (p > 0.05, d < 0.4, AUC < 0.7 for all). However, for short-term segments, AI indicated better distinguishability for both tasks, i.e., for task i, p < 0.001, d = 0.71, AUC = 0.71; for task ii, p < 0.001, d = 0.93, AUC = 0.74. The rest three measures all failed with small effect sizes and AUC values (d < 0.5, AUC < 0.7 for all) although the difference in SI for task i was statistically significant (p < 0.001). Besides, AI displayed smaller variations across different short-term segments, indicating more robust performance. Results from the second short-term analysis were in keeping with those findings. CONCLUSION: The proposed AI indicated better performance especially for short-term heartbeat interval data, suggesting potential in the ambulatory application of cardiovascular monitoring. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12938-017-0402-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5607847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56078472017-09-24 Area asymmetry of heart rate variability signal Yan, Chang Li, Peng Ji, Lizhen Yao, Lianke Karmakar, Chandan Liu, Changchun Biomed Eng Online Research BACKGROUND: Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series. METHOD: An area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta’s index (PI), Guzik’s index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from >3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG. RESULTS: For long-term data, SI showed acceptable performance for both tasks, i.e., for task i p < 0.001, Cohen’s d = 0.93, AUC (area under the receiver-operating characteristic curve) = 0.86; for task ii p < 0.001, d = 0.88, AUC = 0.75. AI performed well for task ii (p < 0.001, d = 1.0, AUC = 0.78); for task i, though the difference was statistically significant (p < 0.001, AUC = 0.76), the effect size was small (d = 0.11). PI and GI failed in both tasks (p > 0.05, d < 0.4, AUC < 0.7 for all). However, for short-term segments, AI indicated better distinguishability for both tasks, i.e., for task i, p < 0.001, d = 0.71, AUC = 0.71; for task ii, p < 0.001, d = 0.93, AUC = 0.74. The rest three measures all failed with small effect sizes and AUC values (d < 0.5, AUC < 0.7 for all) although the difference in SI for task i was statistically significant (p < 0.001). Besides, AI displayed smaller variations across different short-term segments, indicating more robust performance. Results from the second short-term analysis were in keeping with those findings. CONCLUSION: The proposed AI indicated better performance especially for short-term heartbeat interval data, suggesting potential in the ambulatory application of cardiovascular monitoring. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12938-017-0402-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-21 /pmc/articles/PMC5607847/ /pubmed/28934961 http://dx.doi.org/10.1186/s12938-017-0402-3 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Yan, Chang Li, Peng Ji, Lizhen Yao, Lianke Karmakar, Chandan Liu, Changchun Area asymmetry of heart rate variability signal |
title | Area asymmetry of heart rate variability signal |
title_full | Area asymmetry of heart rate variability signal |
title_fullStr | Area asymmetry of heart rate variability signal |
title_full_unstemmed | Area asymmetry of heart rate variability signal |
title_short | Area asymmetry of heart rate variability signal |
title_sort | area asymmetry of heart rate variability signal |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607847/ https://www.ncbi.nlm.nih.gov/pubmed/28934961 http://dx.doi.org/10.1186/s12938-017-0402-3 |
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