Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network

  • Abstract
  • Keywords
  • References
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  • Abstract

    Myocardial infarction is an irreversible damage of heart muscle caused by prolonged oxygen deficiency. As a result, the presence of damaged tissue will alter the normal sinus rhythm. Hence, the paper proposes to profile history of myocardial infarction from electrocardiogram using artificial neural network. Data for anterior and inferior myocardial infarction, as well as healthy control is acquired from PTB Diagnostic ECG Database. Subsequently, QRS power ratio features for different frequency zones are extracted from the pre-processed electrocardiogram. Discriminative ability of the features is assessed using k-nearest neighbor. The best combination of features with 99.7% testing accuracy is the power ratio composite that combines both low-frequency and mid-frequency information. An intelligent profiling model is successfully developed using the composite features and an optimized artificial neural network. The model was able to identify between different electrocardiogram groups with overall accuracy of 98.4% and mean squared error of less than 0.1. Conclusively, the proposed signal processing approach has provided an improved alternative to the established methods from literature.



  • Keywords

    Myocardial Infarction; ECG, Power Ratio; k-Nearest Neighbor; Artificial Neural Network.

  • References

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Article ID: 21392
DOI: 10.14419/ijet.v7i4.11.21392

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