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Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

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  • Narin, Ali
  • Isler, Yalcin
  • Ozer, Mahmut
  • Perc, Matjaž

Abstract

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-minute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events.

Suggested Citation

  • Narin, Ali & Isler, Yalcin & Ozer, Mahmut & Perc, Matjaž, 2018. "Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 56-65.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:56-65
    DOI: 10.1016/j.physa.2018.06.022
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    References listed on IDEAS

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    1. Stanley Nattel, 2002. "New ideas about atrial fibrillation 50 years on," Nature, Nature, vol. 415(6868), pages 219-226, January.
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    Cited by:

    1. Isler, Yalcin & Narin, Ali & Ozer, Mahmut & Perc, Matjaž, 2019. "Multi-stage classification of congestive heart failure based on short-term heart rate variability," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 145-151.
    2. Boaretto, B.R.R. & Andreani, A.C. & Lopes, S.R. & Prado, T.L. & Macau, E.E.N., 2024. "The use of entropy of recurrence microstates and artificial intelligence to detect cardiac arrhythmia in ECG records," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    3. Yang, Chuanzuo & Luan, Guoming & Liu, Zhao & Wang, Qingyun, 2019. "Dynamical analysis of epileptic characteristics based on recurrence quantification of SEEG recordings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 507-515.
    4. Sujata Dash & Ajith Abraham & Ashish Kr Luhach & Jolanta Mizera-Pietraszko & Joel JPC Rodrigues, 2020. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.
    5. Fatma Murat & Ferhat Sadak & Ozal Yildirim & Muhammed Talo & Ender Murat & Murat Karabatak & Yakup Demir & Ru-San Tan & U. Rajendra Acharya, 2021. "Review of Deep Learning-Based Atrial Fibrillation Detection Studies," IJERPH, MDPI, vol. 18(21), pages 1-17, October.

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