Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals

Yun Chen, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8% and specificity of 92.8%, which show superior performance (i.e., 5.6% improvements) to the single-scale recurrence analysis.

Original languageEnglish (US)
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages2595-2598
Number of pages4
DOIs
StatePublished - Oct 31 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period7/3/137/7/13

Fingerprint

Wavelet Analysis
Recurrence
Nonlinear Dynamics
Electrocardiography
Time series
Time delay
Lead
Experiments
Myocardial Infarction
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Chen, Y., & Yang, H. (2013). Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 2595-2598). [6610071] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6610071
Chen, Yun ; Yang, Hui. / Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. pp. 2595-2598 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).
@inproceedings{28c0dedb9ac44488847859e2569a3552,
title = "Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals",
abstract = "Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8{\%} and specificity of 92.8{\%}, which show superior performance (i.e., 5.6{\%} improvements) to the single-scale recurrence analysis.",
author = "Yun Chen and Hui Yang",
year = "2013",
month = "10",
day = "31",
doi = "10.1109/EMBC.2013.6610071",
language = "English (US)",
isbn = "9781457702167",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
pages = "2595--2598",
booktitle = "2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013",

}

Chen, Y & Yang, H 2013, Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals. in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013., 6610071, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 2595-2598, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, 7/3/13. https://doi.org/10.1109/EMBC.2013.6610071

Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals. / Chen, Yun; Yang, Hui.

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. p. 2595-2598 6610071 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals

AU - Chen, Yun

AU - Yang, Hui

PY - 2013/10/31

Y1 - 2013/10/31

N2 - Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8% and specificity of 92.8%, which show superior performance (i.e., 5.6% improvements) to the single-scale recurrence analysis.

AB - Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8% and specificity of 92.8%, which show superior performance (i.e., 5.6% improvements) to the single-scale recurrence analysis.

UR - http://www.scopus.com/inward/record.url?scp=84886557167&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886557167&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2013.6610071

DO - 10.1109/EMBC.2013.6610071

M3 - Conference contribution

SN - 9781457702167

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 2595

EP - 2598

BT - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013

ER -

Chen Y, Yang H. Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. p. 2595-2598. 6610071. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6610071