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Prediction of atrial fibrillation based on nonlinear modeling of heart rate variability signal and SVM classifier

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Abstract

Purpose

Cardiac arrhythmia is one of the major causes of death worldwide. Atrial fibrillation (AF) is considered as the most prevalent sustained cardiac arrhythmia. It increases the risk of cardiac stroke and heart failure. This study aims to present an automated technique for AF detection by analyzing the ECG signal so that individual heart condition can be monitored accurately and an alarm system can be simulated if any serious cardiac abnormality occurs.

Methods

The heart rate variability (HRV) signal reflects the fluctuation of heart in different time intervals. The proposed algorithm includes nonlinear methods for characterizing the dynamics of HRV signal to find diagnosis pattern for AF detection. The diagnostically relevant nonlinear parameters are extracted from HRV signal. The extracted features are subjected to decision tree and support vector machine (SVM) classifier to discriminate AF from normal heart condition.

Results

The experimental result is evaluated on 25 ECG data set of AF and 54 ECG data sets of normal subjects taken from Physionet database to illustrate the diagnostic ability of the classifiers. The tenfold cross-validation method is also applied for performance evaluation. The proposed algorithm has achieved an average accuracy of 99.11%, sensitivity, specificity, and F-score values of 98.92%, 99.25%, and 99.08%, respectively, using SVM classifier which is better than the result obtained from decision tree classifier having average accuracy of 96.41% and F-score of 95.71%.

Conclusion

The proposed algorithm provides great potential for AF diagnosis with high accuracy. This work yields superior performance based on the comparative study with the existing scientific approaches to categorize AF from normal ones.

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References

  • Abdul-Kadir NA, Safri NM, Othman MA. Dynamic ECG features for atrial fibrillation recognition. Comput Methods Programs Biomed. 2016;136:143–50.

    Article  Google Scholar 

  • Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci. 2017;405:81–90.

    Article  Google Scholar 

  • Acharya UR, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Lim CM. Characterization of focal EEG signals: a review. Futur Gener Comput Syst. 2018;91:290–9.

    Article  Google Scholar 

  • Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med. 2015;60:132–42.

    Article  Google Scholar 

  • Ashkenazy Y, Baker DR, Gildor H, Havlin S. Nonlinearity and multifractality of climate change in the past 420000 years. Res Lett. 2003;23:2146–9.

    Google Scholar 

  • Bodruzzaman M, Cadzow J, Shiavi R, Kilroy A, Dawant B, Wilkes M. Hurst’s rescaled-range (R/S) analysis and fractal dimension of electromyographic (EMG) signal. In: IEEE proceedings of the Southeastcon, Williamsburg, USA. 1991. p. 7803–7833

  • Chamoli A, Bansal AR, Dimri VP. Wavelet and rescaled range approach for the Hurst coefficient for short and long time series. Comput Geo-Sci. 2006;33(1):83–93.

    Article  Google Scholar 

  • D’Aloia M, Longo A, Rizzi M. Noisy ECG signal analysis for automatic peak detection. Information. 2019;10(2):35. https://doi.org/10.3390/info10020035.

    Article  Google Scholar 

  • Daqrouq K, Alkhateeb A, Ajour MN, Morfeq A. Neural network and wavelet average framing percentage energy for atrial fibrillation classification. Comput Methods Programs Biomed. 2014;113:919–26.

    Article  Google Scholar 

  • Ebrahimzadeh E, Kalantari M, Joulani M, Shahraki RS, Fayaz F, Ahmadi F. Prediction of paroxysmal atrial fibrillation: a machine learning based approach using combined feature vector and mixture of expert classification on HRV signal. Comput Methods Programs Biomed. 2018. https://doi.org/10.1016/j.cmpb.2018.07.014.

    Article  Google Scholar 

  • Eckmann JP, Ruelle D. Foundation limitations for estimating dimensions and Lyapunov exponents in dynamic systems. Physica D 1992;56(185)

  • Faust O, Shenfield A, Kareem M, San TR, Fujita H, Acharya UR. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med. 2018;102:327–35.

    Article  Google Scholar 

  • Fischer R, Akay M, Castiglioni P. Multi- and monofractal indices of short-term heart rate variability. Med Biol Eng Comput 2003;41:543–549.

  • Gilmore M, Yu CX, Rhodes TL, Peebles WA. Investigation of rescaled range analysis, the Hurst exponent, and long-time correlations in plasma turbulence. Phys Plasmas. 2002;9(4):1312–7.

    Article  Google Scholar 

  • Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the Anticoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001;285:2370–5.

    Article  Google Scholar 

  • Goldberger AL, Mietus, Moody GB, Peng CK, Stanley HE. PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:e215–20.

    Google Scholar 

  • Haissaguerre M, Hocini M, Sanders P, Sacher F, Rotter M, Takahashi Y, Jais P. Catheter ablation of long-lasting persistent atrial fibrillation: clinical outcome and mechanisms of subsequent arrhythmias. J Cardiovasc Electrophysiol. 2005;16(11):1138–47. https://doi.org/10.1111/j.1540-8167.2005.00308.

    Article  Google Scholar 

  • Kalidas V, Tamil LS. Detection of atrial fibrillation using discrete-state Markov models and Random Forests. Comput Biol Med. 2019. https://doi.org/10.1016/j.compbiomed.2019.103386.

    Article  Google Scholar 

  • Kora R, Annavarapu A, Yadlapalli P, Krishna KSR, Somalaraju V. ECG based atrial fibrillation detection using sequency ordered complex Hadamard transform and hybrid firefly algorithm. Eng Sci Technol Int J. 2017;20:1084–91.

    Google Scholar 

  • Lee J, Reyes BA, McManus DD, Mathaias O, Chon KH. Atrial fibrillation detection using an iPhone 4S. IEEE Trans Biomed Eng. 2013;60(1):203–6.

    Article  Google Scholar 

  • Mandal S, Sinha N. Arrhythmia diagnosis from ECG signal analysis using statistical features and novel classification method. J Mech Med Biol. 2021;21(03):2150025. https://doi.org/10.1142/S0219519421500251.

    Article  Google Scholar 

  • Mandal S, Mondal P. A Halder Roy, detection of ventricular arrhythmia by using heart rate variability signal and ECG beat image. Biomed Signal Process Control. 2021;68: 102692. https://doi.org/10.1016/j.bspc.2021.102692.

    Article  Google Scholar 

  • Martis RJ, Acharya UR, Prasad H, Chua KC, Lim CM. Automated detection of atrial fibrillation using Bayesian paradigm. Knowl Syst. 2013;54:269275.

    Google Scholar 

  • Marton LF, Brassai ST, Bako L, Losonczi L. Detrended fluctuation analysis of EEG signals. Procedia Technol. 2014;12:125–32.

    Article  Google Scholar 

  • Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia database. IEEE Eng Med Biol. 2001;20(3):45–50.

    Article  Google Scholar 

  • Murphy A, Banerjee A, Breithardt G, Camm AJ, Commerford P, Freedman B, Gonazalez-Harmosillo JA, Halperin JL, Lau CP, Perel P, Xavier D, Wood D, Jouven X, Morillo CA. The world heart federation roadmap for nonvalvular atrial fibrillation. Glob Heart. 2017;12(4):273–84.

    Article  Google Scholar 

  • Nandy A, Alahe MA, Uddin SMN, Alam S, Nahid A-A, Awal MA. Feature extraction and classification of EEG signals for seizure detection. In: International conference on robotics, electrical signal processing techniques (ICREST). 2019.

  • Nattel S, Guasch E, Savelieva I, Cosio FG, Valverde I, Halperin JL, Camm AJ. Early management of atrial fibrillation to prevent cardiovascular complications. Eur Heart J. 2014;35(22):1448–56. https://doi.org/10.1093/eurheartj/ehu028.

    Article  Google Scholar 

  • Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng 1985:230–36.

  • Pavlopoulos SA, Stasis AC, Loukis EN. A decision tree–based method for the differential diagnosis of Aortic Stenosis from Mitral regurgitation using heart sounds. Biomed Eng Online. 2004;3(1):21. https://doi.org/10.1186/1475-925x-3-21.

    Article  Google Scholar 

  • Phothisonothai M, Arita Y, Watanabe K. Effects of time win- dowing for extraction of expression from Japanese speech: Higuchi’s fractal dimen- sion. In: 13th international symposium on communications and information technologies (ISCIT), Surat Thani, Thailand, 2013. 13873342.

  • Pohjalainen J, Rasanen O, Kadioglu S. Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput Speech Lang. 2015;29(1):145–71.

    Article  Google Scholar 

  • Pourbabaee B, Roshtkhari MJ, Khorasani K. Feature leaning with deep convolutional neural networks for screening patients with paroxysmal atrial fibrillation. In: International joint conference on neural networks (IJCNN). 2016, p. 5057–64.

  • Proietti R, Hadjis A, AlTurki A, Thanassoulis G, Roux JF, Verma A, Essebag V. A systematic review on the progression of paroxysmal to persistent atrial fibrillation. JACC. 2015;1(3):105–15. https://doi.org/10.1016/j.jacep.2015.04.010.

    Article  Google Scholar 

  • Ribeiro IJS, Pereira R, Valença Neto PF, Freire IV, Casotti CA, dos Reis MG. Relationship between diabetes mellitus and heart rate variability in community-dwelling elders. Medicina. 2017;53(6):375–9. https://doi.org/10.1016/j.medici.2017.12.001.

    Article  Google Scholar 

  • Rudy Y, Plonsey R. The eccentric spheres model as the basis for a study of the role of geometry and inhomogeneities in electrocardiography. IEEE Trans Biomed Eng. 1979;26(7):392–9. https://doi.org/10.1109/tbme.1979.326417.

    Article  Google Scholar 

  • Sayantan G, Kien PT, Kadambari KV. Classification of ECG beats using deep belief network and active learning. Med Biol Eng Comput. 2018;56:1887–98.

    Article  Google Scholar 

  • Singh V, Gupta A, Sohal JS, Singh A. Multi-scale fractal dimension to quantify heart ratevariability and systolic blood pressure variability: a postural stress analysis. Fluct Noise Lett. 2019;18(4):1950019.

    Article  Google Scholar 

  • Sinha N, Das A. Analysis of ECG signal based on feature fusion and two-fold classification approach. In: 2021 international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT). 2021, p. 1–5. https://doi.org/10.1109/ICAECT49130.2021.9392515.

  • Sinha N, Mandal S. Diagnosis of congestive heart failure from HRV signal using SVM classifier and patient specific cross validation. In: International journal of innovative technology and exploring engineering (IJITEE), Vol. 9, No. 3. 2020.

  • Sridhar C, Lih OS, Jahmunah V. Accurate detection of myocardial infarction using non linear features with ECG signals. J Ambient Intell Human Comput. 2021;12:3227–44. https://doi.org/10.1007/s12652-020-02536-4.

    Article  Google Scholar 

  • Stein PK, Ehsani AA, Domitrovich PP, Kleiger RE, Rottman JN. The effect of exercise training on heart rate variability in healthy older adults. Am Heart J. 1999;138:567–76.

    Article  Google Scholar 

  • Tripathy RK, Paternina MRA, Arrieta JG, Attanaik PP. Automated detection of atrial fibrillation ECG signals using two stage VMD and atrial fibrillation diagnosis index. J Mech Med Biol 2017;17(7):1740044.

  • Wolf A, Swift JB, Swinney HL, Vastano JA. Determining Lyapunov exponents from a time series. Physica D. 1985;16(3):285–317.

    Article  MathSciNet  Google Scholar 

  • Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 1991;22:983–8.

    Article  Google Scholar 

  • Yuan X, Yuan Y, Huang Y, Liand X, Li W. Multifractal detrended fluctuation analysis of electric load series. Fractals. 2015;23(02):01–10.

    Article  Google Scholar 

  • Zhao L, Liu C, Wei S, Shen Q, Zhou F, Li J. A new entropy-based atrial fibrillation detection method for scanning wearable ECG recordings. Entropy. 2018;20(12):904.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Radio Physics and Electronics, University of Calcutta, Kolkata, West Bengal, India

    Saurav Mandal & Nabanita Sinha

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  1. Saurav Mandal
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Correspondence to Saurav Mandal.

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Mandal, S., Sinha, N. Prediction of atrial fibrillation based on nonlinear modeling of heart rate variability signal and SVM classifier. Res. Biomed. Eng. 37, 725–736 (2021). https://doi.org/10.1007/s42600-021-00175-y

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