ArticleBiomedical engineering online2017
Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy.
Article in Biomedical engineering online, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of atrial fibrillation using heart rate variability: a meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Heart rate variability provides prognostic value in multiple system atrophy.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2026Article
- Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations.Frontiers in cardiovascular medicine · 2025Review
- On the development of diagnostic support algorithms based on CPET biosignals dataPeerJ. Computer science · 2025Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning.Cardiovascular digital health journal · 2022Article
- Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data.Frontiers in cardiovascular medicine · 2022Review
- A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia.Sensors (Basel, Switzerland) · 2021Article
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
- Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction.
resultsThe performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively.
conclusionsOur proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.
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