ArticleJournal of clinical medicine2021
An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 84 citations in OpenAlex.
- Article
- Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.Diagnostics (Basel, Switzerland) · 2025Article
- Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset.Diagnostics (Basel, Switzerland) · 2025Article
- Wearable ECG Device and Machine Learning for Heart Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.NPJ digital medicine · 2024Article
- Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer.Journal of cancer research and clinical oncology · 2023Article
- DDM-HSA: Dual Deterministic Model-Based Heart Sound Analysis for Daily Life Monitoring.Sensors (Basel, Switzerland) · 2023Article
- An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal.Diagnostics (Basel, Switzerland) · 2022Article
- A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection.Journal of clinical medicine · 2022Article
- Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.Physical and engineering sciences in medicine · 2022Article
- Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements.Sensors (Basel, Switzerland) · 2022Article
- A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia.Journal of imaging · 2022Article
- ECG Data Analysis with Denoising Approach and Customized CNNs.Sensors (Basel, Switzerland) · 2022Article
- Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.