ArticleComputational and mathematical methods in medicine2018
Arrhythmia Classification of ECG Signals Using Hybrid Features.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations.Frontiers in cardiovascular medicine · 2025Review
- Article
- What to consider for ECG in mice-with special emphasis on telemetry.Mammalian genome : official journal of the International Mammalian Genome Society · 2023Review
- GAN-based patient information hiding for an ECG authentication system.Biomedical engineering letters · 2023Article
- Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.Sensors (Basel, Switzerland) · 2023Article
- Characterization and Validation of Flexible Dry Electrodes for Wearable Integration.Sensors (Basel, Switzerland) · 2023Article
- A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques.Bioinformatics and biology insights · 2023Review
- A novel automated junctional ectopic tachycardia detection tool for children with congenital heart disease.Heart rhythm O2 · 2022Article
- Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet.Computational intelligence and neuroscience · 2022Article
- A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG.Emergency medicine international · 2022Article
- A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.BMC medical informatics and decision making · 2021Article
- Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
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Abstract
Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.
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