ArticleJournal of medical systems2016
Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.Sensors (Basel, Switzerland) · 2024Article
- Improving performance of classifiers for diagnosis of critical diseases to prevent COVID risk.Computers & electrical engineering : an international journal · 2022Article
- Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult.Frontiers in molecular biosciences · 2022Article
- Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression.Frontiers in oncology · 2022Article
- A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.BMC medical informatics and decision making · 2021Article
- Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier.Scientific reports · 2021Article
- An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.Journal of healthcare engineering · 2021Article
- Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model.Computational and mathematical methods in medicine · 2021Article
- EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms.Journal of biomedical research · 2020Article
- Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method.Sensors (Basel, Switzerland) · 2019Article
- Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering.Sensors (Basel, Switzerland) · 2019Article
- Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.Journal of medical Internet research · 2019Observational
- Accurate and rapid screening model for potential diabetes mellitus.BMC medical informatics and decision making · 2019Article
- Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2018Article
- Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.Computational and mathematical methods in medicine · 2018Article
- A Statistical Classifier to Support Diagnose Meningitis in Less Developed Areas of Brazil.Journal of medical systems · 2017Article
- Wavelet decomposition analysis in the two-flash multifocal ERG in early glaucoma: a comparison to ganglion cell analysis and visual field.Documenta ophthalmologica. Advances in ophthalmology · 2017Article
Corrections and comments
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2 authors.
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Abstract
In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F-measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).
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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 OpenQuestion graph.