Abstract
The tremendous load on the rather archaic medical system in developing countries has necessitated the need to implement artificial intelligence-enabled automated systems to classify different kinds of electrocardiogram (ECG) traces. To this end, we are proposing a novel R-based open-source software with inherent capability to classify different kinds of automated geometric visualizations along with its categorization based upon similarity indices as measured by earth mover’s distance (EMD). This innovative automated software needs verification and validation by clinical practitioners/cardiologists before being implemented to classify large ECG databases to enhance its machine learning capabilities. We anticipate that integration of this robust automated classifier with divergent platforms such as mobile health applications would enable the subjects/patients to continuously monitor the heart rate themselves.
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This work is supported by a fellowship grant from Indian Council of Medical Research, New Delhi.
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Shrivastav, K.D. et al. (2020). Earth Mover’s Distance-Based Automated Geometric Visualization/Classification of Electrocardiogram Signals. In: Sarma, H., Bhuyan, B., Borah, S., Dutta, N. (eds) Trends in Communication, Cloud, and Big Data. Lecture Notes in Networks and Systems, vol 99. Springer, Singapore. https://doi.org/10.1007/978-981-15-1624-5_8
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