2011
DOI: 10.1177/0954411911425839
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Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space

Abstract: Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge … Show more

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Cited by 10 publications
(6 citation statements)
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References 62 publications
(125 reference statements)
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“…The false-nearest neighbor algorithm (FNN) is a technique that quantifies the points which appear close together in a low dimension that are actually far apart in a RPS with higher dimension (Kennel et al 1992). Using FNN and an area enclosed by the outer loop (Parvaneh et al 2012) embedding dimension and a delay equal to two and four samples, respectively, were found for creating normalized ECG RPS. Then, the RPS was divided into small pixels (20 × 20 square grids).…”
Section: Post-processingmentioning
confidence: 99%
See 1 more Smart Citation
“…The false-nearest neighbor algorithm (FNN) is a technique that quantifies the points which appear close together in a low dimension that are actually far apart in a RPS with higher dimension (Kennel et al 1992). Using FNN and an area enclosed by the outer loop (Parvaneh et al 2012) embedding dimension and a delay equal to two and four samples, respectively, were found for creating normalized ECG RPS. Then, the RPS was divided into small pixels (20 × 20 square grids).…”
Section: Post-processingmentioning
confidence: 99%
“…The number of points in each square was normalized to the total number of points and the normalized numbers were considered as features (400 features). (e) Poincare section from ECG (13 features): reconstructed RPS explained in feature group (d) were used to extract 13 different features from an intersection of the ECG trajectory in RPS with a unity line (Poincare section of ECG (Parvaneh et al 2012(Parvaneh et al , 2016). Details on features and their estimation can be found in the original article (Parvaneh et al 2012).…”
Section: Post-processingmentioning
confidence: 99%
“…As an example, extracted features from reconstructed phase space of ECG were proposed to capture changes in morphology of ECG due to AF through comparison of features between AF and NSR [13,14].…”
Section: Traditional Machine Learning For Automated Ecg Interpretationmentioning
confidence: 99%
“…Furthermore, AF incidence and prevalence are likely to increase, especially with the aging of the population. ECG-based AF detectors mostly analyze atrial activities (e.g., absence of P waves) [2][3][4], ventricular responses (e.g., irregularity in RR intervals) [5][6][7], or both. Different features from RR intervals, such as heart rate variability (HRV), geometric representation, and entropy are used to differentiate between AF and Normal Sinus Rhythm (NSR) [7,8].…”
Section: Introductionmentioning
confidence: 99%