Comparison of 1D Convolutional Neural Networks for Arrhythmias Classification Using ECG Signals

Abstract:

Cardiovascular Diseases (CVD), encompassing a range of heart and blood vessel conditions, have long been a significant global health concern. Among these, arrhythmias, disruptions in the heart’s rhythm, hold substantial importance due to their potential impact on morbidity and mortality. Atrial fibrillation (AF), the most common arrhythmia worldwide, affects millions and is associated with increased morbidity and mortality. This study focuses on enhancing arrhythmia detection using neural networks and hyperparameter tuning. By exploring various model dimensions, layers, batch sizes, and optimizers, we rigorously evaluated their impact on performance using electrocardiogram (ECG) signal data. Results showed that a hybrid CNN+LSTM architecture with 6 layers, utilizing the Adam optimizer and a batch size of 32, achieved the best accuracy in arrhythmia detection. These findings emphasize the importance of hyperparameter tuning for effective model generalization and its potential to improve cardiac care in the face of significant global health challenges posed by cardiovascular diseases, including arrhythmias.

Año de publicación:

2024

Keywords:

• arrhythmia
• Cardiovascular disease
• Convolutional neural network (CNN)
• electrocardiogram
• Long short-term memory (LSTM)

Fuente:

scopus