ArticleComputer methods and programs in biomedicine2020
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.
Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 130 citations in OpenAlex.
- Personalized Deep Networks for Enhanced ECG Segmentation.Cardiovascular engineering and technology · 2026Article
- Cardiac arrhythmia detection via PQRST analyzed data using an optimized hierarchical fused fuzzy deep reinforcement learning.BMC medical informatics and decision making · 2026Article
- Contactless Cardiac Health Monitoring with Millimeter-Wave Radar Based on PMG-SATNet.Sensors (Basel, Switzerland) · 2026Article
- Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks.Bioengineering (Basel, Switzerland) · 2026Article
- Challenges in the Classification of Cardiac Arrhythmias and Ischemia Using End-to-End Deep Learning and the Electrocardiogram: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis.NPJ digital medicine · 2025Article
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.Sensors (Basel, Switzerland) · 2025Article
- Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.PloS one · 2025Article
- An Enhanced Hybrid Model Combining CNN, BiLSTM, and Attention Mechanism for ECG Segment Classification.Biomedical engineering and computational biology · 2025Article
- Research on a Lightweight Arrhythmia Classification Model Based on Knowledge Distillation for Wearable Single-Lead ECG Monitoring Systems.Sensors (Basel, Switzerland) · 2024Article
- Article
- Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.BMC medical research methodology · 2024Article
- A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG.Scientific reports · 2023Article
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- The efficacy of detecting arrhythmia is higher with 7-day continuous electrocardiographic patch monitoring than with 24-h Holter monitoring.Journal of arrhythmia · 2023Article
- Deep Learning Algorithms for Estimation of Demographic and Anthropometric Features from Electrocardiograms.Journal of clinical medicine · 2023Article
- Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases.Journal of personalized medicine · 2023Article
- Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms.Bioengineering (Basel, Switzerland) · 2023Article
- A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG.Frontiers in physiology · 2023Article
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Authors and funding
5 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveCardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database.
methodsOur DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers.
resultsWe performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively.
conclusionRecently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.
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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.