Key result
1D CNN achieves 100% accuracy in classifying atrial fibrillation from ECG signals.
Why the study?
Diagnosing atrial fibrillation from ECG recordings takes a long time and requires specialists to analyze signal patterns.
A 1D CNN deep learning model can classify atrial fibrillation from ECG signals with perfect accuracy in this dataset, potentially automating and accelerating diagnosis.
Does not support clinical adoption of 1D CNN for AF detection; leaves open generalizability beyond this dataset.
Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
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Fachrurrozi et al. (2023) studied Atrial Fibrillation. 1-Dimensional Convolutional Neural Network (CNN 1D) was evaluated on Model performance (accuracy, precision, recall, F1 Score). A 1-Dimensional Convolutional Neural Network (CNN 1D) developed to classify atrial fibrillation in ECG signals achieved 100% accuracy, precision, recall, and F1 Score.
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