Wazed, SafwanChowdhury, Mubasshir S.Sian, MuhtasimRahman, AtaurMahmud, TasfinShawon, Md. Mehedi HasanRahim, A.H.M.A.2026-08-252026-08-252024-01-01S. Wazed et al., "Deep Learning-Based Arrhythmia Detection Using Convolutional Neural Network," 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), Pune, India, 2024, pp. 1-6, doi: 10.1109/I2CT61223.2024.10543561.97983503944742-s2.0-85196843699https://hdl.handle.net/10361/29510In this paper, we present a convolutional neural network-based deep learning model that can classify different classes of arrhythmia from an ECG dataset. The model is trained on the Shaoxing People's Hospital dataset, which contains ECG records for more than 10,000 people. After analysis, an accuracy of 84% was achieved using the created deep CNN algorithm. Furthermore, performance comparisons of different models, including support vector machine (SVM), K-nearest neighbors (KNN), random forests (RF), and an ensemble model, have been presented.6 Pagesen-USSupport vector machinesRadio frequencyDeep learningArrhythmiaForestryElectrocardiographyElectrocardiogram (ECG)Random forestElectrocardiography.Arrhythmia--Diagnosis.Conference Proceeding10.1109/I2CT57861.2023.10126334