IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v509y2018icp56-65.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118307465
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.06.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stanley Nattel, 2002. "New ideas about atrial fibrillation 50 years on," Nature, Nature, vol. 415(6868), pages 219-226, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael A Colman, 2019. "Arrhythmia mechanisms and spontaneous calcium release: Bi-directional coupling between re-entrant and focal excitation," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-34, August.
    2. Daniel M Lombardo & Flavio H Fenton & Sanjiv M Narayan & Wouter-Jan Rappel, 2016. "Comparison of Detailed and Simplified Models of Human Atrial Myocytes to Recapitulate Patient Specific Properties," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-15, August.
    3. Eberhard P Scholz & Paola Carrillo-Bustamante & Fathima Fischer & Mathias Wilhelms & Edgar Zitron & Olaf Dössel & Hugo A Katus & Gunnar Seemann, 2013. "Rotor Termination Is Critically Dependent on Kinetic Properties of IKur Inhibitors in an In Silico Model of Chronic Atrial Fibrillation," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.
    4. Cui, Xingran & Chang, Hung-Chi & Lin, Lian-Yu & Yu, Chih-Chieh & Hsieh, Wan-Hsin & Li, Weihui & Peng, Chung-Kang & Lin, Jiunn-Lee & Lo, Men-Tzung, 2019. "Prediction of atrial fibrillation recurrence before catheter ablation using an adaptive nonlinear and non-stationary surface ECG analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 9-19.
    5. Feng Ou & Nini Rao & Xudong Jiang & Mengyao Qian & Wei Feng & Lixue Yin & Xu Chen, 2013. "Analysis on Differential Gene Expression Data for Prediction of New Biological Features in Permanent Atrial Fibrillation," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-8, October.
    6. Yan-yan Li & Chuan-wei Zhou & Jian Xu & Yun Qian & Bei Wang, 2012. "CYP11B2 T-344C Gene Polymorphism and Atrial Fibrillation: A Meta-Analysis of 2,758 Subjects," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:56-65. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.