AUTOMATIC DETECTION OF ATRIAL FIBRILLATION BASED ON RR INTERVAL

Yıl 2019, , 487 - 497, 15.09.2019

Anıl Can Güzeler Süleyman Bilgin

https://doi.org/10.21923/jesd.512030

Öz

Heart diseases are rapidly increasing worldwide and in our country. This
increase causes difficulties in the diagnosis processes of heart diseases. Considering
these problems, the studies of engineering applications related to medical
science give effective results in terms of solutions. By means of engineering
devices and algorithms, positive contributions are made to medical
applications. These contributions assist physicians especially in the diagnosis
stages and speed up these processes. In this study, a new algorithm is
developped so that Atrial Fibrillation (AF), which is the most common type of
arrhythmia encountered, can be automatically detected at a high success rate.
Electrocardiogram (ECG) data used in this study were obtained from physiobank ATM
database. 31 samples of Atrial Fibrillation Rhythm (AFR) and 31 samples of
Normal Sinus Rhythm (NSR) were obtained from this database. RR Interval (RRI)
sequences being 12 hours long are used in the study. The change of the RRI
sequences is an important parameter for AF. The RRI sequences are re-sampled using
signal pre-processing techniques. The Discrete Wavelet Transform (DWT) was then
applied to the resampled signals. In this way, feature extraction process is
performed and the wavelet energies of these signals are visually examined with
boxplot. The wavelet energies of the RRI sequences are classified by the Support
Vector Machine (SVM). Finally, AFR and NSR are successfully separated as 99.60%
achievement.

Anahtar Kelimeler

Atrial fibrillation, Discrete wavelet transform, Support vector machine, Normal sinus rhythm, RR interval

ATRİYAL FİBRİLASYONUN RR ARALIĞI İLE OTOMATİK TESPİTİ

Öz

Kalp hastalıkları dünya
genelinde ve ülkemizde hızlı bir biçimde artmaktadır. Bu artış kalp
hastalıklarının tanı süreçlerinde zorlukların oluşmasına neden olmaktadır. Bu
sorunlar düşünüldüğünde mühendislik uygulamalarının tıp bilimi ile ilgili olan
çalışmaları çözümler açısından etkili sonuçlar vermektedir. Mühendislik
sayesinde geliştirilen cihazlar ve algoritmalar sayesinde tıp uygulamalarına
olumlu katkılar sağlanmaktadır. Bu uygulamalar özellikle hekimlere tanı
aşamalarında yardımcı olmakta ve bu süreçleri hızlandırmaktadır. Bu çalışmada
en sık rastlanan aritmi çeşidi olarak karşımıza çıkan Atriyal Fibrilasyon’un
(AF) otomatik olarak tespitinin yüksek başarı oranında yapılması
tasarlanmıştır. Bu çalışmada kullanılan Elektrokardiyogram (EKG) verileri, Phsiyobank
ATM veritabanından elde edilmiştir. Bu veritabınından 31 adet Atriyal
Fibrilasyon Ritmi (AFR) ve 31 adet Normal Sinüs Ritmi (NSR) olan sinyaller
alınmıştır. Bu sinyaller 12’şer saatlik uzunlukta olup çalışmada RR Aralıkları
dizileri kullanılmıştır. RRA dizilerinin değişimi AF için önemli bir parametre
olarak karşımıza çıkmaktadır. Sinyal işleme teknikleri ile RR Aralıkları zaman
ekseninde yeniden örneklenmiştir. Ardından yeniden örneklenen sinyallere Ayrık
Dalgacık Dönüşümü (ADD) uygulanmıştır. Bu sayede özellik çıkarımı işlemi
yapılmış ve bu sinyallerin dalgacık enerjileri boxplot ile görsel olarak
incelenmiştir. RRA dizilerinin dalgacık enerjileri Destek Vektör Makinası (DVM)
ile sınıflandırma işlemine tabi tutulmuş ve %99,60 oranında başarıyla AFR ve
NSR birbirinden ayrılmıştır. 

Anahtar Kelimeler

Atriyal fibrilasyon, Ayrık dalgacık dönüşümü, Destek vektör makinası, Normal sinüs ritim, RR aralığı

Kaynakça

• Annavarapu, A. & Kora, P. 2016. ECG-based atrial fibrillation detection using different orderings of Conjugate Symmetric–Complex Hadamard Transform. International Journal of the Cardiovascular Academy, 2, 151-154.
• Anonymous 1: The Long-Term AF Database [Online]. Available: https://physionet.org/physiobank/database/ltafdb/. [last access date: 01.12.2017]
• Anonymous 2: The MIT-BIH Normal Sinus Rhythm Database [Online]. Available: https://physionet.org/physiobank/database/nsrdb/. [last access date: 01.12.2017]
• Anonymous 3: Normal Sinus Rhythm RR Interval Database [Online]. Available: https://physionet.org/physiobank/database/nsr2db/.[ last access date: 01.12.2017]
• Asgari, S., Mehrnia, A. & Moussavi, M. 2015. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Computers in biology and medicine, 60, 132-142.
• Ayers, B., Beshaw, C., Serrano-Finetti, E., Casas, O., Pallas-Areny, R. & Couderc, J.-P. 2016. Enabling atrial fibrillation detection using a weight scale. Computing in Cardiology Conference (CinC), IEEE, 969-972.
• Bilgin, S. 2008. Kalp Hızı Değişkenliğinin Dalgacık Dönüşümü ve Yapay Sinir Ağları Kullanılarak Analizi. PhD Thesis, Sakarya Univercity.
• Camm, A. J., Malik, M., Bigger, J., Breithardt, G., Cerutti, S., Cohen, R., Coumel, P., Fallen, E., Kennedy, H. & Kleiger, R. 1996. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93, 1043-1065.
• Carrara, M., Carozzi, L., Moss, T. J., De Pasquale, M., Cerutti, S., Lake, D. E., Moorman, J. R. & Ferrario, M. 2015. Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT–BIH databases. Journal of electrocardiology, 48, 943-946.
• Chen, S.-W. 2002. A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia. IEEE transactions on biomedical engineering, 49, 736-742.
• Clinic., C. 2017. Atrial Fibrillation (Afib): Treatment Options [Online]. Available: https://www.clevelandclinic.org/heart. [Son erişim tarihi: 05.11.2017]
• Colloca, R. 2012. Implementation and Testing of Atrial Fibrillation Detectors for A Mobile Phone Application. Master Thesis, Politecnico Di Milano.
• Dakos, G., Konstantinou, D., Chatzizisis, Y. S., Chouvarda, I., Filos, D., Paraskevaidis, S., Mantziari, L., Maglaveras, N., Karvounis, H. & Vassilikos, V. 2015. P wave analysis with wavelets identifies hypertensive patients at risk of recurrence of atrial fibrillation: A case–control study and 1year follow-up. Journal of electrocardiology, 48, 845-852.
• Deshmukh, A., Brown, M. L., Higgins, E., Schousek, B., Abeyratne, A., Rovaris, G. & Friedman, P. A. 2016. Performance of Atrial Fibrillation Detection in a New Single‐Chamber ICD. Pacing and Clinical Electrophysiology, 39, 1031-1037.
• Felix, J., Alcaraz, R. & Rieta, J. 2015. Adaptive wavelets applied to automatic local activationwave detection in fractionated atrial electrograms of atrial fibrillation. Computing in Cardiology Conference (CinC), IEEE, 45-48.
• García, M., Ródenas, J., Alcaraz, R. & Rieta, J. J. 2016. Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation. computer methods and programs in biomedicine, 131, 157-168.
• Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K. & Stanley, H. E. 2000. Physiobank, physiotoolkit, and physionet. Circulation, 101, e215-e220.
• Gumbinger, C., Krumsdorf, U., Veltkamp, R., Hacke, W. & Ringleb, P. 2012. Continuous monitoring versus HOLTER ECG for detection of atrial fibrillation in patients with stroke. European journal of neurology, 19, 253-257.
• Gutiérrez-Gnecchi, J. A., Morfin-Magaña, R., Lorias-Espinoza, D., Del Carmen Tellez-Anguiano, A., Reyes-Archundia, E., Méndez-Patiño, A. & Castañeda-MIRANDA, R. 2017. DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomedical Signal Processing and Control, 32, 44-56.
• Güzeler, A. C. 2017. Holter Ekg İşaretleri Üzerinden Otomatik Atrial Fibrilasyon Tespiti. Master Thesis. Akdeniz University.
• Haeberlin, A., Roten, L., Schilling, M., Scarcia, F., Niederhauser, T., Vogel, R., Fuhrer, J. & Tanner, H. 2014. Software-based detection of atrial fibrillation in long-term ECGs. Heart rhythm, 11, 933-938.
• Hurnanen, T., Lehtonen, E., Tadi, M. J., Kuusela, T., Kiviniemi, T., Saraste, A., Vasankari, T., Airaksinen, J., Koivisto, T. & Pankaala, M. 2017. Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiagrams, IEEE journal of biomedical and health informatics.
• Islam, M. S., Ammour, N., Alajlan, N. & Aboalsamh, H. 2016. Rhythm-based heartbeat duration normalization for atrial fibrillation detection. Computers in biology and medicine, 72, 160-169.
• Islam, S., Ammour, N. & Alajlan, N. 2017. Atrial fibrillation detection with multiparametric RR interval feature and machine learning technique. Informatics, Health & Technology (ICIHT), International Conference on. IEEE, 1-5.
• Jiang, K., Huang, C., Ye, S.-M. & Chen, H. 2012. High accuracy in automatic detection of atrial fibrillation for Holter monitoring. Journal of Zhejiang University-Science B, 13, 751-756.
• Johura, F. T., Islam, S. M. R., Maniruzzaman, M. & Hasan, M. 2017. ECG signal for artrial fibrillation detection. Electrical, Computer and Communication Engineering (ECCE), International Conference on, IEEE, 928-934.
• Kennedy, A., Finlay, D. D., Guldenring, D., Bond, R. R., Moran, K. & Mclaughlin, J. 2016. Automated detection of atrial fibrillation using RR intervals and multivariate-based classification. Journal of electrocardiology, 49, 871-876.
• Kim, M. S., Kim, Y. N. & Cho, Y. C. 2015. Electrocardiographic characteristics of significant factors of detected atrial fibrillation using WEMS., 20, 37-46.
• Kirchhof, P., Benussi, S., Kotecha, D., Ahlsson, A., Atar, D., Casadei, B., Castella, M., Diener, H.-C., Heidbuchel, H. & Hendriks, J. 2016. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. European heart journal, 37, 2893-2962.
• Kora, P., Annavarapu, A., Yadlapalli, P., Krishna, K. S. R. & Somalaraju, V. 2017. ECG based Atrial Fibrillation detection using Sequency Ordered Complex Hadamard Transform and Hybrid Firefly Algorithm. Engineering Science and Technology, an International Journal.
• Kora, P. & Krishna, K. S. R. 2016. ECG based heart arrhythmia detection using wavelet coherence and bat algorithm. Sensing and Imaging, 17, 1-16.
• Ladavich, S. & Ghoraani, B. 2015. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomedical Signal Processing and Control, 18, 274-281.
• Lee, S. H., Myoung, H. S., Kang, C. H., Choi, E. K. & Lee, K. J. 2016. Amplitude based beat detection for atrial fibrillation in pacemaker. Engineering in Medicine and Biology Society (EMBC), IEEE 38th Annual International Conference of the, IEEE, 2757-2759.
• Mabrouki, R., Khaddoumi, B. & Sayadi, M. 2016. Atrial Fibrillation detection on electrocardiogram. Advanced Technologies for Signal and Image Processing (ATSIP), 2nd International Conference on, IEEE, 268-272.
• Malik, M. 1996. Heart rate variability. Circulation, 93, 1043-1065.
• Marsili, I., Masè, M., Pisetta, V., Ricciardi, E., Andrighetti, A. O., Ravelli, F. & Nollo, G. 2016. Optimized algorithms for atrial fibrillation detection by wearable tele-holter devices. Smart Cities Conference (ISC2), IEEE International, IEEE, 1-4.
• Martis, R. J., Acharya, U. R., Prasad, H., Chua, C. K. & Lim, C. M. 2013. Automated detection of atrial fibrillation using Bayesian paradigm. Knowledge-Based Systems, 54, 269-275.
• Mcgill, R., Tukey, J. W. & Larsen, W. A. 1978. Variations of box plots. The American Statistician, 32, 12-16.
• Mittal, S., Rogers, J., Sarkar, S., Koehler, J., Warman, E. N., Tomson, T. T. & Passman, R. S. 2016. Real-world performance of an enhanced atrial fibrillation detection algorithm in an insertable cardiac monitor. Heart Rhythm, 13, 1624-1630.
• Morris, F., Edhouse, J., Brady, W. J. & Camm, J. 2003. Abc of Clinical Elektrocardiography, London, BMJ Books, 89 s.
• Nuryani, N., Harjito, B., Yahya, I. & Lestari, A. 2015. Atrial fibrillation detection using support vector machine. Electric Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering (ICEVT & IMECE), 2015 Joint International Conference, IEEE, 215-218.
• Oster, J. & Clifford, G. D. 2015. Impact of the presence of noise on RR interval-based atrial fibrillation detection. Journal of electrocardiology, 48, 947-951.
• Padmavathi, K. & Ramakrishna, K. S. 2015. Detection of Atrial Fibrillation using Autoregressive modeling. International Journal of Electrical and Computer Engineering (IJECE), 5, 64-70.
• Patro, K. K., Kumar, P. R. & Viswanadham, T. 2016. An efficient signal processing algorithm for accurate detection of characteristic points in Abnormal ECG signals. Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on, IEEE, 1476-1479.
• Petrutiu, S., Sahakian, A. V. & Swiryn, S. 2007. Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans. Europace, 9, 466-470.
• Rivera, D., Veiga, C., Rodríguez-Andina, J. J., Fariña, J. & García, E. 2017. Using support vector machines for atrial fibrillation screening. Industrial Electronics (ISIE), IEEE 26th International Symposium on, 2017. IEEE, 2056-2060.
• Ródenas, J., García, M., Alcaraz, R. & Rieta, J. J. 2015. Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy, 17, 6179-6199.
• Ros, E., Mota, S., Fernández, F., Toro, F. & Bernier, J. L. 2004. ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm. Computers in biology and medicine, 34, 679-696.
• Saalasti, S. 2003. Neural networks for heart rate time series analysis, Jyväskylän yliopisto.
• Sanders, P., Pürerfellner, H., Pokushalov, E., Sarkar, S., Di bacco, M., Maus, B., Dekker, L. R. & Investigators, R. L. U. 2016. Performance of a new atrial fibrillation detection algorithm in a miniaturized insertable cardiac monitor: Results from the Reveal LINQ Usability Study. Heart Rhythm, 13, 1425-1430.
• Sejr, M. H., Nielsen, J. C., Damgaard, D., Sandal, B. F. & May, O. 2017. Atrial fibrillation detected by external loop recording for seven days or two-day simultaneous Holter recording: A comparison in patients with ischemic stroke or transient ischemic attack. Journal of electrocardiology, 50, 287-293.
• Shan, S.-M., Tang, S.-C., Huang, P.-W., LIN, Y.-M., Huang, W.-H., LAI, D.-M. & WU, A.-Y. A. 2016. Reliable PPG-based algorithm in atrial fibrillation detection. Biomedical Circuits and Systems Conference (BioCAS), IEEE, 340-343.
• Yoon, K. H., Thap, T., Jeong, C. W., Kim, N. H., Noh, S., Nam, Y. & Lee, J. 2015. Analysis of Statistical Methods for Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series. Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 9th International Conference on, IEEE, 452-457.
• Yuan, C., Yan, Y., Zhou, L., Bai, J. & Wang, L. 2016. Automated atrial fibrillation detection based on deep learning network. Information and Automation (ICIA), 2016 IEEE International Conference on, IEEE, 1159-1164.