An Approach to a Rough Set Based Disease Inference Engine for ECG Classification

Mitra, S.; Mitra, Madhuchhanda; Chaudhuri, B. B.

doi:10.1007/11908029_42

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Rough Set Theory and Hidden Markov Models1

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2010

2021

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Cited by 3 publications

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“…Several investigators in the existing literature have utilized rough set theory in computerized ECG analysis in hopes of attaining maximum benefits. One such research conducted by Mitra et al, (2006) demonstrated an optimized approach of rule based rough set for effective diagnosis. A TIFF formatted image database was utilized in the study.…”

Section: Rough Set Theory and Hidden Markov Modelsmentioning

confidence: 99%

A Survey on various Machine Learning Approaches for ECG Analysis

Roopa¹,

Harish²

2017

IJCA

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Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing most of the cardiac disease. The main objective of this paper is to review the various machine learning approaches for diagnosing Myocardial Infarction (heart attack), differentiate Arrhythmias (heart beat variation), Hypertrophy (increase thickness of the heart muscle) and Enlargement of Heart. Further, we also present various machine learning approaches and compare different methods and results used to analyze the ECG. The existing methods are compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained.

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Section: Rough Set Theory and Hidden Markov Modelsmentioning

confidence: 99%