ArticleComputational intelligence and neuroscience2021
Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnosing the benign paroxysmal positional vertigo via 1D and deep-learning composite model.Journal of neurology · 2023Article
- Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring.iScience · 2023Article
- A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques.Bioinformatics and biology insights · 2023Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.Bioengineering (Basel, Switzerland) · 2022Article
- A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.BioMed research international · 2022Article
- Mixed Script Identification Using Automated DNN Hyperparameter Optimization.Computational intelligence and neuroscience · 2021Article
- Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy.Computational intelligence and neuroscience · 2021Article
- DPEA-Net: a clinically-oriented lightweight 3D CNN for glioma segmentation in multiparametric MRI.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning techniques on the publicly available dataset to classify arrhythmia. We have used two kinds of the dataset in our research paper. One dataset is the MIT-BIH arrhythmia database, with a sampling frequency of 125 Hz with 1,09,446 ECG beats. The classes included in this first dataset are N, S, V, F, and Q. The second database is PTB Diagnostic ECG Database. The second database has two classes. The techniques used in these two datasets are the CNN model, CNN + LSTM, and CNN + LSTM + Attention Model. 80% of the data is used for the training, and the remaining 20% is used for testing. The result achieved by using these three techniques shows the accuracy of 99.12% for the CNN model, 99.3% for CNN + LSTM, and 99.29% for CNN + LSTM + Attention Model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.