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Area asymmetry of heart rate variability signal

Yan, Chang, Li, Peng, Ji, Lizhen, Yao, Lianke, Karmakar, Chandan and Liu, Changchun 2017, Area asymmetry of heart rate variability signal, Biomedican engineering online, vol. 16, pp. 1-14, doi: 10.1186/s12938-017-0402-3.

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Title Area asymmetry of heart rate variability signal
Author(s) Yan, Chang
Li, Peng
Ji, Lizhen
Yao, Lianke
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Liu, Changchun
Journal name Biomedican engineering online
Volume number 16
Article ID 112
Start page 1
End page 14
Total pages 14
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2017-09-21
ISSN 1475-925X
Keyword(s) Area asymmetry
Heart rate asymmetry (HRA)
Heart rate variability (HRV)
Phase asymmetry
Poincaré plot
Summary 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.
Language eng
DOI 10.1186/s12938-017-0402-3
Field of Research 080109 Pattern Recognition and Data Mining
090609 Signal Processing
090399 Biomedical Engineering not elsewhere classified
0903 Biomedical Engineering
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30104211

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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