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A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset

A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset

Dinesh Bhatia, Animesh Mishra
Copyright: © 2020 |Pages: 25
ISBN13: 9781799821205|ISBN10: 179982120X|EISBN13: 9781799821229
DOI: 10.4018/978-1-7998-2120-5.ch003
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MLA

Bhatia, Dinesh, and Animesh Mishra. "A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset." Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering, edited by Dilip Singh Sisodia, et al., IGI Global, 2020, pp. 42-66. https://doi.org/10.4018/978-1-7998-2120-5.ch003

APA

Bhatia, D. & Mishra, A. (2020). A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset. In D. Sisodia, R. Pachori, & L. Garg (Eds.), Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering (pp. 42-66). IGI Global. https://doi.org/10.4018/978-1-7998-2120-5.ch003

Chicago

Bhatia, Dinesh, and Animesh Mishra. "A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset." In Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering, edited by Dilip Singh Sisodia, Ram Bilas Pachori, and Lalit Garg, 42-66. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2120-5.ch003

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Abstract

The role of ECG analysis in the diagnosis of cardio-vascular ailments has been significant in recent times. Although effective, the present computational algorithms lack accuracy, and no technique till date is capable of predicting the onset of a CVD condition with precision. In this chapter, the authors attempt to formulate a novel mapping technique based on feature extraction using fractional Fourier transform (FrFT) and map generation using self-organizing maps (SOM). FrFT feature extraction from the ECG data has been performed in a manner reminiscent of short time Fourier transform (STFT). Results show capability to generate maps from the isolated ECG wavetrains with better prediction capability to ascertain the onset of CVDs, which is not possible using conventional algorithms. Promising results provide the ability to visualize the data in a time evolution manner with the help of maps and histograms to predict onset of different CVD conditions and the ability to generate the required output with unsupervised training helping in greater generalization than previous reported techniques.

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