A deep neural network ensemble classifier with focal loss for automatic arrhythmia classification

H Wu, S Zhang, B Bao, J Li, Y Zhang… - Journal of Healthcare …, 2022 - Wiley Online Library
Automated electrocardiogram classification techniques play an important role in assisting
physicians in diagnosing arrhythmia. Among these, the automatic classification of single …

Classification of arrhythmia ecg signals using convolutional neural network

M Habijan, I Galić, A Pizurica - 2023 30th International …, 2023 - ieeexplore.ieee.org
The electrocardiogram (ECG) has been established as a reliable tool for monitoring
cardiovascular health. Vast amount of ECG recordings can pose a challenge for its …

Towards end-to-end ECG classification with raw signal extraction and deep neural networks

SS Xu, MW Mak, CC Cheung - IEEE journal of biomedical and …, 2018 - ieeexplore.ieee.org
This paper proposes deep learning methods with signal alignment that facilitate the end-to-
end classification of raw electrocardiogram (ECG) signals into heartbeat types, ie, normal …

Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

S Mousavi, F Afghah - ICASSP 2019-2019 IEEE international …, 2019 - ieeexplore.ieee.org
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and
diagnose several abnormal arrhythmias. While there have been remarkable improvements …

WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

N Kim, W Seo, J Kim, SY Choi, SM Park - Computer Methods and Programs …, 2023 - Elsevier
Background and objective Automated detection of arrhythmias from electrocardiograms
(ECGs) can be of considerable assistance to medical professionals in providing efficient …

A transformer-based deep neural network for arrhythmia detection using continuous ECG signals

R Hu, J Chen, L Zhou - Computers in Biology and Medicine, 2022 - Elsevier
Recently, much effort has been put into solving arrhythmia classification problems with
machine learning-based methods. However, inter-heartbeat dependencies have been …

An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise separable convolutional neural networks

E Ihsanto, K Ramli, D Sudiana, TS Gunawan - Applied Sciences, 2020 - mdpi.com
Many algorithms have been developed for automated electrocardiogram (ECG)
classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to …

Deep learning based patient-specific classification of arrhythmia on ECG signal

W Zhao, J Hu, D Jia, H Wang, Z Li… - 2019 41st Annual …, 2019 - ieeexplore.ieee.org
The classification of the heartbeat type is an essential function in the automatical
electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined …

[HTML][HTML] Inter-patient arrhythmia classification with improved deep residual convolutional neural network

Y Li, R Qian, K Li - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Abstract Background and Objective: Early detection of arrhythmias has become critical due
to the increased mortality from cardiovascular disease, and ECG is an effective tool for …

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks

J Lim, D Han, MPS Nejad, KH Chon - Computers in Biology and Medicine, 2024 - Elsevier
We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG)
signal analysis, addressing both waveform delineation and beat type classification tasks. For …