Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yan, Chang, Li, Peng, Ji, Lizhen, Yao, Lianke, Karmakar, Chandan, Liu, Changchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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
_version_ 1783265352054996992
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
work_keys_str_mv AT yanchang areaasymmetryofheartratevariabilitysignal
AT lipeng areaasymmetryofheartratevariabilitysignal
AT jilizhen areaasymmetryofheartratevariabilitysignal
AT yaolianke areaasymmetryofheartratevariabilitysignal
AT karmakarchandan areaasymmetryofheartratevariabilitysignal
AT liuchangchun areaasymmetryofheartratevariabilitysignal