A two-stage solution algorithm for paroxysmal atrial fibrillation prediction

Lynn, Ke-Shiuan; Chiang, Hsiao‐Dong

doi:10.1109/cic.2001.977678

Cited by 17 publications

(9 citation statements)

References 6 publications

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“…The overall sensitivity, specificity, and positive predictivity of the proposed method in predicting the PAF events were 96.55%, 100%, and 100%, respectively. Comparing the performance of the proposed algorithm to those of the previously reported methods in the literature shows Yang et al [42] Footprint analysis 57 -Lynn et al [36] Return map and difference map of RR intervals…”

Section: Resultsmentioning

confidence: 96%

“…All of these methods used AFPDB database to evaluate their approaches. The authors of [6,36,38,42] participated in Computers in Cardiology Challenge 2001 (PAF prediction challenge) and Zong et al [6] could achieve the highest score. They used the number and timing of PACs as their main discriminator to predict PAF events and achieved a sensitivity of 79% for predicting the onset of PAF.…”

Section: Resultsmentioning

confidence: 99%

“…Different classification methods have been used for AF classification and prediction in the past. [9,[35][36][37][38][39][40] In this work, the SVM classifier is used for classifying the episodes before PAF and far from PAF.…”

Section: Classificationmentioning

confidence: 99%

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Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation

2011

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Development of accurate predictors of the onset of PAF is clinically important because of the increasing possibility of electrically stabilizing and preventing the onset of atrial arrhythmias with different atrial pacing techniques. [4] The maintenance of sinus rhythm can lead to decreased symptoms and possibly a decrease in the atrial remodeling that causes increased susceptibility to future episodes of PAF. [5] In addition, there may be a reduction in the risk of strokes and thromboembolic events.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

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Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation

2011

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“…This would possibly give better results than the ones expected using a valid test set. In [16] the authors used a -nearest neighbor classifier and reported no results on the training set. Different kinds of features are used in each approach: Heart rate variability (HRV) features, power spectral density of the wave and of HRV, wave morphology, atrial premature complexes (APCs) number and timing.…”

Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

Evolutionary Algorithms for Multiobjective and Multimodal Optimization of Diagnostic Schemes

Toro¹,

Ros

Mota

et al. 2006

IEEE Trans. Biomed. Eng.

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Abstract-This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of definable characteristics extracted from the biosignal source followed by the specific diagnostic scheme is presented. In this framework, multiobjective evolutionary algorithms are used to meet not only classification accuracy but also other objectives of medical interest, which can be conflicting. Furthermore, the use of both multimodal and multiobjective evolutionary optimization algorithms provides the medical specialist with different alternatives for configuring the diagnostic scheme. Some application examples of this methodology are described in the diagnosis of a specific cardiac disorder-paroxysmal atrial fibrillation.Index Terms-Evolutionary algorithms, multiobjective and multimodal optimization, noninvasive medical diagnosis, paroxysmal atrial fibrillation.

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“…Patients with PAF give no sign before having an attack generally. On the other hand, there are some methods developed to predict PAF events in the literature including the frequency of atrial premature difference (Zong et al 2001), the P-wave changes in 60-second segments of data (Schrier et al 2001) atrial ectopic and ventricular ectopic numbers using RR intervals (Langley, et al 2001), Heart Rate Variability (HRV) time domain measures (SDNN, pNN50, RMSSD), frequency domain (Wavelet Transform) and the number of atrial and ventricular ectopic beats on data segment of 1 min, 5 min and 30 minutes (Maier et al 2001), time domain measurements (1-6 correlation coefficients, NN50, pNN50, RMSSD, SDSD), frequency domain measurements the Fast Fourier Transform (FFT) using RR interval, P-wave shape, a power spectral density of the P-wave (Chazal and Heneghan 2001), HRV measurements with two-level algorithm (Lynn and Chiang 2001), FFT using RR interval and calculate the RR interval dynamics (Krstacic et al 2001) footprint analysis (Yang and Yin 2001), Spectrum, bispectrum and nonlinear measurements (Mohebbi and Ghassemian 2012), spectral changes of P wave (Alcaraz et al 2015) and Poincare plot and based on Wavelet transform measurements from HRV (Park et al 2009), standard deviation and spectral entropy measures on RR interval data (Thuraisingham 2016), spectral analysis, approximate entropy (ApEn), sample entropy (SamEn) and multiscale complexity analysis using HRV signals (Chesnokov 2008) and time domain, frequency domain, non-linear and bispectrum measures from HRV data (Boon, et al 2016), linear and non-linear HRV measurements (Narin et al 2016, Narin et al 2017).…”

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confidence: 99%

Prediction of PAF Attacks using Time-Domain Measures of Heart Rate Variability

Narin

İşler

Özer

2018

KSEJ

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Paroxysmal atrial fibrillation (PAF) is the mainly encountered type of arrhythmia and there is no validated method to predict a PAF attack before it occurs. In this study, predicting the PAF event was aimed using time-domain heart rate variability (HRV) measures in k- nearest neighbor (k-nn) classifier. Traditional time-domain HRV measures were analyzed in every 5-minute segments from 49 normal subjects, 25 patients with PAF attack and 25 patients with no attack within 45 minutes. All features were investigated whether they showed statistically significance. Significant features were classified by k-nn for odd numbers of neighbors between 1 and 19. This setup was run with two different configurations as study 1 to discriminate patients with PAF attack from normals and patients with no attack, and study 2 to discriminate patients with PAF attack from patients with no attack. SDNN, RMSSD and pNN50 measures were found to show statistically significant differences with p less than 0.05 in segments of 0-5 min, 2.5-7.5 min and 5-10 min intervals only. The maximum classification accuracy was obtained in the time interval of 2.5-7.5 minutes with %79 for Study 1 and just before attack with %80 for Study 2 in the time interval of 0-5 minutes. Results showed that the prediction of PAF events was possible when the classification between normal subjects from PAF patients was accurate. PAF attack can be determined 2.5 minutes earlier by simple classifier algorithms.

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A two-stage solution algorithm for paroxysmal atrial fibrillation prediction

Cited by 17 publications

References 6 publications

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation

Evolutionary Algorithms for Multiobjective and Multimodal Optimization of Diagnostic Schemes

Prediction of PAF Attacks using Time-Domain Measures of Heart Rate Variability

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