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Heart beat classification from single-lead ECG using the synchrosqueezing transform.

Publication ,  Journal Article
Herry, CL; Frasch, M; Seely, AJ; Wu, H-T
Published in: Physiological measurement
February 2017

The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fetal ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modelled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sentivities and positive predictive values comparable to other published algorithms using multiple leads and many more features.

Duke Scholars

Published In

Physiological measurement

DOI

EISSN

1361-6579

ISSN

0967-3334

Publication Date

February 2017

Volume

38

Issue

2

Start / End Page

171 / 187

Related Subject Headings

  • Signal Processing, Computer-Assisted
  • Humans
  • Heart Rate
  • Electrocardiography
  • Biomedical Engineering
  • Algorithms
  • 4003 Biomedical engineering
  • 3208 Medical physiology
  • 1116 Medical Physiology
  • 0906 Electrical and Electronic Engineering
 

Citation

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Herry, C. L., Frasch, M., Seely, A. J., & Wu, H.-T. (2017). Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiological Measurement, 38(2), 171–187. https://doi.org/10.1088/1361-6579/aa5070
Herry, Christophe L., Martin Frasch, Andrew Je Seely, and Hau-Tieng Wu. “Heart beat classification from single-lead ECG using the synchrosqueezing transform.Physiological Measurement 38, no. 2 (February 2017): 171–87. https://doi.org/10.1088/1361-6579/aa5070.
Herry CL, Frasch M, Seely AJ, Wu H-T. Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiological measurement. 2017 Feb;38(2):171–87.
Herry, Christophe L., et al. “Heart beat classification from single-lead ECG using the synchrosqueezing transform.Physiological Measurement, vol. 38, no. 2, Feb. 2017, pp. 171–87. Epmc, doi:10.1088/1361-6579/aa5070.
Herry CL, Frasch M, Seely AJ, Wu H-T. Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiological measurement. 2017 Feb;38(2):171–187.
Journal cover image

Published In

Physiological measurement

DOI

EISSN

1361-6579

ISSN

0967-3334

Publication Date

February 2017

Volume

38

Issue

2

Start / End Page

171 / 187

Related Subject Headings

  • Signal Processing, Computer-Assisted
  • Humans
  • Heart Rate
  • Electrocardiography
  • Biomedical Engineering
  • Algorithms
  • 4003 Biomedical engineering
  • 3208 Medical physiology
  • 1116 Medical Physiology
  • 0906 Electrical and Electronic Engineering