Abstract
Myocardial infarction (MI), more commonly known as heart attack, is a predominant cause of mortality all over the world. Automated MI identification techniques aid in early detection, thus ensuring timely medication and prevention. The vector-cardiogram (VCG) proves to be a more informative and low dimensional alternative for the 12 lead Electrocardiogram (ECG). The automated VCG analysis tools, reported till date, utilize a large number of features based on the sizes, area and orientation of the QRS and the T loops. Such features are not only difficult to extract but also suffers from the curse of dimensionality. This paper proposes a novel VCG feature - the volume ratio of the 3-d QRS and the ST-T loop, which combines both the loop morphologies into a single feature. Statistical analysis of this feature extracted from the PTB diagnostic ECG database reveals that it is significantly different for the healthy and infarction data and provides a MI detection sensitivity of 98.8%. This study is indicative of the strong utility of this new feature for automated MI classification algorithms.
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Acknowledgements
The first author acknowledges the financial support obtained in the form of DST INSPIRE Fellowship provided by the Department of Science & Technology, Government of India.
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Sadhukhan, D., Datta, J., Pal, S., Mitra, M. (2019). Automated Identification of Myocardial Infarction Using a Single Vectorcardiographic Feature. In: Chattopadhyay, S., Roy, T., Sengupta, S., Berger-Vachon, C. (eds) Modelling and Simulation in Science, Technology and Engineering Mathematics. MS-17 2017. Advances in Intelligent Systems and Computing, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-74808-5_57
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