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Automated detection of myocardial infarction using robust features extracted from 12-lead ECG

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Abstract

Electrocardiography is a useful diagnostic tool for various cardiovascular diseases, such as myocardial infarction (MI). An electrocardiograph (ECG) records the electrical activity of the heart, which can reflect any abnormal activity. MI recognition by visual examination of an ECG requires an expert’s interpretation and is difficult because of the short duration and small amplitude of the changes in ECG signals associated with MI. Therefore, we propose a new method for the automatic detection of MI using ECG signals. In this study, we used maximal overlap discrete wavelet transform to decompose the data, extracted the variance, inter-quartile range, Pearson correlation coefficient, Hoeffding’s D correlation coefficient and Shannon entropy of the wavelet coefficients and used the k-nearest neighbor model to detect MI. The accuracy, sensitivity and specificity of the model were 99.57%, 99.82% and 98.79%, respectively. Therefore, the system can be used in clinics to help diagnose MI.

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Acknowledgements

The work described in this paper is supported by the National Natural Science Foundation of China (NSFC, 81773545).

Author information

ZL wrote the paper and performed experiments. JZ, YG, YC, QG and GM offered useful suggestions for the paper preparation and writing. All authors have read and approved the final manuscript.

Correspondence to Zhuochen Lin.

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The authors declare no conflict of interest.

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Cite this article

Lin, Z., Gao, Y., Chen, Y. et al. Automated detection of myocardial infarction using robust features extracted from 12-lead ECG. SIViP (2020) doi:10.1007/s11760-019-01617-y

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Keywords

  • Electrocardiograph
  • Myocardial infarction
  • Wavelet transform
  • Feature extraction