ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks
This work addresses arrhythmia detection for medical diagnosis, but it is incremental as it applies a known deep learning method to a standard dataset.
The researchers tackled the problem of detecting cardiovascular arrhythmia from multi-lead ECG data using a convolutional neural network with a residual block, achieving a classification accuracy of 98.2% on 15,000 cases from the MIT-BIH dataset.
Arrhythmia is just one of the many cardiovascular illnesses that have been extensively studied throughout the years. Using multi-lead ECG data, this research describes a deep learning (DL) pipeline technique based on convolutional neural network (CNN) algorithms to detect cardiovascular lar arrhythmia in patients. The suggested model architecture has hidden layers with a residual block in addition to the input and output layers. In this study, the classification of the ECG signals into five main groups, namely: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat (N), are performed. Using the MIT-BIH arrhythmia dataset, we assessed the suggested technique. The findings show that our suggested strategy classified 15,000 cases with a high accuracy of 98.2%