A Multistage Deep Belief Networks Application on Arrhythmia Classification

Year 2016, Volume: 4 Issue: Special Issue-1, 222 - 228, 25.12.2016

Gokhan Altan , Yakup Kutlu Novruz Allahverdı

Abstract

An electrocardiogram (ECG) is a biomedical signal type that determines
the normality and abnormality of heart beats using the electrical activity of
the heart and has a great importance for cardiac disorders. The computer-aided
analysis of biomedical signals has become a fabulous utilization method over
the last years. This study introduces a multistage deep learning classification
model for automatic arrhythmia classification. The proposed model includes a
multi-stage classification system that uses ECG waveforms and the Second Order
Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier
which has a greedy layer wise training with Restricted Boltzmann Machines
algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database
heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals
are filtered with median filters to remove the baseline wander. ECG waveforms
were segmented from long-term ECG signals using a window with a length of 501
data points (R wave centered). The extracted waveforms and elliptical features
from the SODP are utilized as the input of the model.  The proposed DBN-based multistage arrhythmia
classification model has discriminated five types of heartbeats with a high accuracy
rate of 96.10%.

Keywords

Arrhythmia, Deep Belief Networks, DBN, Deep Learning, AAMI, ECG Waveform, Second Order Difference Plot, SODP

References

• WHO. The top 10 causes of death y.y. http://www.who.int/mediacentre/factsheets/fs310/en/ (access: 07 August 2016).
• Webster JG. Medical Instrumentation, Application and Design. 4th baskı. Boston: Houghtoon Mifflin Company; 1978.
• Yeh YC, Chiou CW, Lin HJ. Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Syst Appl 2012;39:1000–10. doi:10.1016/j.eswa.2011.07.101.
• Hamilton P. Open source ECG analysis. Comput Cardiol 2002;29:101–4. doi:10.1109/CIC.2002.1166717.
• Ge D, Srinivasan N, Krishnan SM. Cardiac arrhythmia classification using autoregressive modeling. Biomed Eng Online 2002;1:5. doi:10.1186/1475-925X-1-5.
• Israel SA, Irvine JM, Cheng A, Wiederhold MD, Wiederhold BK. ECG to identify individuals. Pattern Recognit 2005;38:133–42. doi:10.1016/j.patcog.2004.05.014.
• Plataniotis KN, Hatzinakos D, Lee JKM. ECG Biometric Recognition Without Fiducial Detection. 2006 Biometrics Symp. Spec. Sess. Res. Biometric Consort. Conf., IEEE; 2006, s. 1–6. doi:10.1109/BCC.2006.4341628.
• Bengio Y, Delalleau O. Justifying and generalizing contrastive divergence. Neural Comput 2009;21:1601–21. doi:10.1162/neco.2008.11-07-647.
• Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag y.y.;20:45–50.
• Kutlu Y, Kuntalp D. A multi-stage automatic arrhythmia recognition and classification system. Comput Biol Med 2011;41:37–45. doi:10.1016/j.compbiomed.2010.11.003.
• Rajendra Acharya U, Suri JS, Spaan JAE, Krishnan SM. Advances in cardiac signal processing. 2007. doi:10.1007/978-3-540-36675-1.
• M. Gabriel Khan. Rapid ECG Interpretation(Contemporary Cardiology). 3rd editio. Humana Press; 2007.
• Dr Patrick Davey. ECG (electrocardiogram). NetDoctor 2011:1–4.
• Kara S. Sensing of ECG signals and Imaging at the Computer in Real Time. Erciyes University, 1991.
• Yayik A, Kutlu Y. Konjestif kalp yetmezliǧinin ikinci-derece fark harita grafiǧi ile topografik analizi ve teşhisi. 2014 22nd Signal Process. Commun. Appl. Conf. SIU 2014 - Proc., 2014, s. 540–3. doi:10.1109/SIU.2014.6830285.
• Altan G, Kutlu Y. ECG based Human identification using Logspace Grid Analysis of Second Order Difference Plot. Signal Process Commun Appl Conf 2015:1288–91.
• Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy Layer-Wise Training of Deep Networks. Adv Neural Inf Process Syst 2007;19:153. doi:citeulike-article-id:4640046.
• Yan Y, Qin X, Wu Y, Zhang N, Fan J, Wang L. A restricted Boltzmann machine based two-lead electrocardiography classification. 2015 IEEE 12th Int Conf Wearable Implant Body Sens Networks 2015:1–9. doi:10.1109/BSN.2015.7299399.
• Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527–54. doi:10.1162/neco.2006.18.7.1527.
• Lee HS, Cheng Q lan, Thakor N V. ECG waveform analysis by significant point extraction. I. Data reduction. Comput Biomed Res 1987;20:410–27. doi:10.1016/0010-4809(87)90030-9.
• Ververidis D, Kotropoulos C. Sequential forward feature selection with low computational cost. Signal Process. Conf. 2005 13th Eur., vol. 13, 2005, s. 1–4.
• Zhang Z, Dong J, Luo X, Choi KS, Wu X. Heartbeat classification using disease-specific feature selection. Comput Biol Med 2014;46:79–89. doi:10.1016/j.compbiomed.2013.11.019.
• Alajlan N, Bazi Y, Melgani F, Malek S, Bencherif MA. Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods. Signal, Image Video Process 2014;8:931–42. doi:10.1007/s11760-012-0339-8.
• Batra A, Jawa V. Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria. Int J Biol Biomed 2016;1:1–7.
• Melin P, Amezcua J, Valdez F, Castillo O. A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf Sci (Ny) 2014;279:483–97. doi:10.1016/j.ins.2014.04.003.
• Thomas M, Das MK, Ari S. Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEU - Int J Electron Commun 2015;69:715–21. doi:10.1016/j.aeue.2014.12.013.
• Leutheuser H, Gradl S, Kugler P, Anneken L, Arnold M, Achenbach S, vd. Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices. IEEE Eng Med Biol Soc Annu Conf 2014;2014:2690–3. doi:10.1109/EMBC.2014.6944177.
• Yayik A, Altan G, Kutlu Y, Yildirim E, Yildirim S. Görgül Mod Fonksiyonların Eliptik Analizi ile Kongestif Kalp Yetmezliği Teşhisi. Int Conf Electr Electron Eng 2014:632–5.
• Rahhal MM Al, Bazi Y, AlHichri H, Alajlan N, Melgani F, Yager RR. Deep Learning Approach for Active Classification of Electrocardiogram Signals. Inf Sci (Ny) 2016;345:340–54. doi:10.1016/j.ins.2016.01.082.
• Huanhuan M, Yue Z. Classification of Electrocardiogram Signals with Deep Belief Networks. Comput Sci Eng (CSE), 2014 IEEE 17th Int Conf 2014:7–12. doi:10.1109/CSE.2014.36.
• Allahverdi N, Altan G, Kutlu Y. Diagnosis of Coronary Artery Disease Using Deep Belief Networks. 2 Int Conf Eng Nat Sci 2016, Sarajevo, Bosnia, pp:40-46.
• Owis MI, Abou-Zied AH, Youssef a. BM, Kadah YM. Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Trans Biomed Eng 2002;49:733–6. doi:10.1109/TBME.2002.1010858.
• Martis RJ, Acharya UR, Lim CM, Suri JS. Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowledge-Based Syst 2013;45:76–82. doi:10.1016/j.knosys.2013.02.007.
• Kim J, Min SD, Lee M. An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects. Biomed Eng Online 2011;10:56. doi:10.1186/1475-925X-10-56.
• Tadejko P, Rakowski W. Hybrid wavelet-mathematical morphology feature extraction for heartbeat classification. EUROCON 2007 - Int. Conf. Comput. as a Tool, 2007, s. 127–32. doi:10.1109/EURCON.2007.4400676.
• Llamedo M, Martinez JP. Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng 2011;58:616–25. doi:10.1109/TBME.2010.2068048.
• Alvarado AS, Lakshminarayan C, Príncipe JC. Time-based compression and classification of heartbeats. IEEE Trans Biomed Eng 2012;59:1641–8. doi:10.1109/TBME.2012.2191407.
• Ye C, Vijaya Kumar BVK, Coimbra MT. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng 2012;59:2930–41. doi:10.1109/TBME.2012.2213253.
• Kutlu Y, Altan G, Allahverdi N. ARRHYTHMIA CLASSIFICATION USING WAVEFORM ECG SIGNALS. 3rd Int. Conf. Adv. Technol. Sci., Konya: 2016, s. 233–239.