ArticlePhysiological measurement2018
A comparison of entropy approaches for AF discrimination.
Article in Physiological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- mRMEBP: a unified framework for online detection of atrial fibrillation utilizing deep learning.npj biomedical innovations · 2024Article
- Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.Sensors (Basel, Switzerland) · 2024Article
- pRR30, pRR3.25% and Asymmetrical Entropy Descriptors in Atrial Fibrillation Detection.Entropy (Basel, Switzerland) · 2024Article
- [Artificial intelligence in wearable electrocardiogram monitoring].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network.Frontiers in physiology · 2023Article
- Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection?Sensors (Basel, Switzerland) · 2022Article
- Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection.Frontiers in physiology · 2022Article
- Enhanced Laterality Index: A Novel Measure for Hemispheric Asymmetry.Journal of healthcare engineering · 2022Article
- Determination of Parameters for an Entropy-Based Atrial Fibrillation Detector.Entropy (Basel, Switzerland) · 2021Article
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
- HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification.Frontiers in physiology · 2021Article
- Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.Medical & biological engineering & computing · 2021Article
- Entropy Profiling: A Reduced-Parametric Measure of Kolmogorov-Sinai Entropy from Short-Term HRV Signal.Entropy (Basel, Switzerland) · 2020Article
- Optimal length of R-R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning.Biomedical engineering online · 2020Article
- A Review of Atrial Fibrillation Detection Methods as a Service.International journal of environmental research and public health · 2020Review
- Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy.Entropy (Basel, Switzerland) · 2020Article
- A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings.Entropy (Basel, Switzerland) · 2018Article
- An open source benchmarked toolbox for cardiovascular waveform and interval analysis.Physiological measurement · 2018Article
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Authors and funding
7 authors.
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Abstract
objectiveThis study focuses on the comparison of single entropy measures for ventricular response analysis-based AF detection. APPROACH: To enhance the performance of entropy-based AF detectors, we developed a normalized fuzzy entropy, [Formula: see text], a novel metric that (1) uses a fuzzy function to determine vector similarity, (2) replaces probability estimation with density estimation for entropy approximation, (3) utilizes a flexible distance threshold parameter, and (4) adjusts for heart rate by subtracting the natural log value of the mean RR interval. An AF detector based on [Formula: see text] was trained using the MIT-BIH atrial fibrillation (AF) database, and tested on the MIT-BIH normal sinus rhythm (NSR) and MIT-BIH arrhythmia databases. The [Formula: see text]-based AF detector was compared to AF detectors based on three other entropy measures: sample entropy ([Formula: see text]), fuzzy measure entropy ([Formula: see text]) and coefficient of sample entropy ([Formula: see text]), over three standard window sizes. MAIN
resultsTo classify AF and non-AF rhythms, [Formula: see text] achieved the highest area under receiver operating characteristic curve (AUC) values of 92.72%, 95.27% and 96.76% for 12-, 30- and 60-beat window lengths respectively. This was higher than the performance of the next best technique, [Formula: see text], over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. [Formula: see text] and [Formula: see text] resulted in lower AUCs (below 90%) over all window sizes. [Formula: see text] also provided superior performance for all other tested statistics, including the Youden index, sensitivity, specificity, accuracy, positive predictivity and negative predictivity. In conclusion, we show that [Formula: see text] can be used to accurately identify AF from RR interval time series. Furthermore, longer window lengths (up to one minute) increase the performance of all entropy-based AF detectors under evaluation except the [Formula: see text]-based method. SIGNIFICANCE: Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.
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