Key result
Deep learning models, particularly Convolutional Neural Networks, achieved high accuracy in classifying cardiac arrhythmias from ECG signals across 25 reviewed studies.
Why the study?
Traditional manual analysis of medical reports for diagnosing disorders is time-consuming, strenuous, and prone to human error, creating a need for efficient autonomous deep learning methods.
This review summarizes recent deep learning methodologies for automated arrhythmia classification using ECG signals, highlighting their potential to reduce human error in diagnosis.
May aid ECG arrhythmia detection while reducing diagnostic error; leaves open prospective validation before clinical adoption.
In most hospitals, the diagnosis of medical disorders involves the traditional approach of doctors manually analyzing the medical reports of the patient. This method is not only time consuming and strenuous, but is also highly prone to human error. With the advent of deep learning technology, an efficient autonomous diagnosis method holds the possibility of replacing the existing tedious approach. This in turn results in the reduction of human error which is of major concern in the medical industry today. Through this paper, we aim to put forth an articulate review of the different deep learning methodologies, observed in the past four years, to classify arrhythmia using electrocardiogram (ECG) signals.
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Deepali Koppad (2021) conducted a review in Arrhythmia. Deep learning models (CNN, RNN) was evaluated on Arrhythmia classification accuracy. Deep learning models, particularly Convolutional Neural Networks, achieved high accuracy in classifying cardiac arrhythmias from ECG signals across 25 reviewed studies.
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