Key result
A 1D convolutional neural network with a residual block achieved 100% accuracy and 0.15% model loss in classifying five types of cardiac arrhythmias from ECG signals.
Why the study?
To obtain a deep learning neural network architecture to recognize and classify five cardiac signals for implementation in an embedded pre-diagnostic device linked to Holter monitoring.
Absolute Event Rate: 100% vs 100%
A 1D convolutional neural network with a residual block can accurately classify five types of cardiac arrhythmias from ECG signals with minimal model loss.
This article shows a neuronal network for deep learning focused on recognizing and classification five types of cardiac signals (Sinus, Ventricular Tachycardia, Ventricular Fibrillation, Atrial Flutter, and Atrial Fibrillation). The final objective is to obtain an architecture that can be implemented in an embedded system as a pre-diagnostic device linked to a Holter monitoring system. The network was designed using the Keras API programmed in Python, where it is possible to obtain a comparison of different types of networks that vary the presence of a residual block, with the result that the network with said block obtains the best response (100% success rate) and a model loss of approximately 0.15%. On the other hand, a validation by means of confusion matrices was carried out to verify the existence of false positives in the network results and evidence what type of arrhythmia can be presented according to the network output against an input signal through the console.
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Sandoval-Cabrera et al. (2021) studied Cardiac arrhythmias (n=1,341). 1D Convolutional Neural Network with residual block vs. 1D Convolutional Neural Network without residual block was evaluated on Classification accuracy. A 1D convolutional neural network with a residual block achieved 100% accuracy and 0.15% model loss in classifying five types of cardiac arrhythmias from ECG signals.
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