ArticleFrontiers in physiology2021
Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2024Pooled it
- A bibliometric analysis of artificial intelligence applications in heart failure.Journal of thoracic disease · 2026Article
- Estimating blood pressure from the electrocardiogram: findings of a large-scale negative results study.Physiological measurement · 2025Article
- MS-LTCAF: A Multi-Scale Lead-Temporal Co-Attention Framework for ECG Arrhythmia Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- [A lightweight classification network for single-lead atrial fibrillation based on depthwise separable convolution and attention mechanism].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.ACS omega · 2024Article
- Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV).BMC medical informatics and decision making · 2024Article
- Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation.Scientific reports · 2024Article
- Non-Monotonic Complexity of Stochastic Model of the Channel Gating Dynamics.Entropy (Basel, Switzerland) · 2023Article
- An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Sympathetic Vagal Balance and Cognitive Performance in Young Adults during the NIH Cognitive Test.Journal of functional morphology and kinesiology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on ECG signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each condition of ECG signal. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, due to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying the discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN), was investigated to provide the best system performance. This study obtained an accuracy of 100% for the k-NN, SVM, RF, and ANN classifiers, respectively, and 97% for the RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.
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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.