Cloud-Enabled Deep Learning for Arrhythmia Classification using 2D ECG Spectral Images

Abstract: Accurate classification of arrhythmias is critical for timely and effective treatment. In this study, we propose a novel approach for arrhythmia classification using 2D spectral images generated from 1D electrocardiogram (ECG) signals. We utilized deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to classify ECG signals into various types of arrhythmias. The proposed approach was evaluated on a large ECG dataset and achieved a high accuracy rate of 99.16%. Furthermore, we employed cloud computing to enable faster and more efficient model training and validation. Our approach has the potential to improve the accuracy and speed of arrhythmia classification and enable remote diagnosis and monitoring of patients using cloud-based platforms. Keywords: Cloud computing, deep learning, arrhythmia classification, ECG, spectral images.