Why the study?
Automated computational diagnostic methods using deep learning are needed to reduce detection time and improve upon conventional machine learning methods for detecting cardiac abnormalities from ECG signals.
Do deep learning techniques improve the classification performance of ECG signals for detecting multiple heart diseases compared to conventional machine learning methods?
Do deep learning techniques improve the classification performance of ECG signals for detecting multiple heart diseases compared to conventional machine learning methods?
Answers are grounded in this paper and cite their sources.
Deep learning techniques show potential to improve the automated detection and classification of multiple heart diseases from ECG signals compared to traditional machine learning methods.
Cardiology is a group of disease that affect the heart and its vessels.All heart conditions characterized by blockage of blood vessels, myocardial problems, valve malfunctions and varied heart rhythms are called heart abnormalities.The heart disease is one of the proven causes of death worldwide.Mortality resulting from heart disease can be reduced if the ailments are detected in an initial stage which helps in treating the patients on time.Cardiac abnormalities are reflected in the morphological features of the 12-lead clinical ECG signal.A lesser degree of detection time for doctors to analyze longterm electrocardiogram data and detect slight deviations in the electrocardiogram morphology.Automated computational diagnostic methods using deep learning techniques need to be developed to improve the performance of conventional machine learning based methods used for cardiac disease detection.The review article is therefore intended to present a detailed overview of the work on different computer aided automated methods used by many researchers from many years to automatically detect various heart ailments by characterizing and classifying ECG signal.This work includes a brief introduction on major heart ailments including CAD, MI, CHF, Cardiomyopathy, their typical ECG patterns and characteristics.The credibility of traditional computer aided methods used to detect multiple heart ailments is explored and further the deep learning techniques required to improve the performance of existing methods is discussed.From the results obtained by many researchers it is also revealed that the classification performance is to be improved by using deep learning techniques.
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Padmavathi et al. (2023) studied this question.
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