Key result
Deep learning techniques, particularly Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), demonstrated great accuracy in correctly classifying major cardiac arrhythmias.
Why the study?
Early diagnosis of cardiac arrhythmias via ECG analysis may aid treatment, prompting a review of detection strategies published between 2018 and 2022.
Do deep learning techniques improve the accuracy of cardiac arrhythmia classification from ECG signals compared to traditional neural networks?
Do deep learning techniques improve the accuracy of cardiac arrhythmia classification from ECG signals compared to traditional neural networks?
Deep learning techniques such as CNN, GRU, and LSTM provide highly accurate automated classification of cardiac arrhythmias from ECG signals.
Supports further exploration of deep learning for ECG arrhythmia classification; leaves open prospective validation versus traditional methods.
Cardiac arrhythmias are abnormal heartbeats that might be irregular, excessively rapid, or too slow. Cardiac arrhythmia occurs when electrical impulses in the heart malfunction. The early diagnosis of cardiac arrhythmias may aid in the cure or treatment of the illness regarding heart. This situation can be detected through biomedical signals such as Electrocardiograms (ECGs), These signals are monitored to identify heart electrical activity through sensors implanted over human body's chest. Heart problems will be recognized by analyzing these ECG signals. The anomaly will be categorized based on the kind of ECG found. This has been considered by numerous researchers, who have suggested some techniques for cardiac arrhythmias detection. The strategies for cardiac arrhythmias detection have been published in the last four years (2018-2022), are the primary topic of this paper. In recent years Machine Learning (ML) and Deep Learning (DL) made remarkable accomplishments in signal processing field. While comparing with traditional neural networks (NN), deep neural networks (DNN) are capable of automatic feature extraction, and recognize intricate patterns of data and also eliminate complex signal processing. Few DNN include Support Vector Machine (SVM), recurrence network, Deep Belief Network (DBN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU). Based on the study, it is observed that CNN is mostly referred as the appropriate feature extraction technique. While using CNN, GRU and LSTM separately, DL techniques demonstrated great accuracy in correctly classifying the major Cardiac Arrhythmia "Atrial-Fibrillation (AF), SupraVentricular Ectopic Beats (SVEB), Ventricular Ectopic Beats (VEB)".
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A et al. (2023) conducted a review in Cardiac Arrhythmia. Machine Learning and Deep Learning techniques (e.g., CNN, GRU, LSTM) vs. Traditional neural networks was evaluated on Classification accuracy of cardiac arrhythmias. Deep learning techniques, particularly Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), demonstrated great accuracy in correctly classifying major cardiac arrhythmias.
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