Comprehensive electrocardiographic diagnosis based on deep learning.

Artificial Intelligence in Medicine
Oh Shu LihU Rajendra Acharya

Abstract

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and v...Continue Reading

Citations

Sep 3, 2020·International Journal of Environmental Research and Public Health·Oliver FaustU Rajendra Acharya
Jan 23, 2021·International Journal of Environmental Research and Public Health·Ningrong LeiOliver Faust
Apr 7, 2021·International Journal of Imaging Systems and Technology·Vicnesh JahmunahU Rajendra Acharya
Jun 3, 2021·International Journal of Environmental Research and Public Health·Manish SharmaU Rajendra Acharya
Jun 28, 2020·Computer Methods and Programs in Biomedicine·Shu Lih OhU Rajendra Acharya

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