ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks
Not on the map. Why?
Abstract (arXiv)
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%
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 7 Mar 2023.
Builds on
3 of 24 references.
References · 3
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approachcited by 15
- 2022A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillancecited by 6
Built on by
Not cited yet.