Noise-tolerant modular neural network system for classifying ECG signal

Alberto Ochoa, Luis J. Mena, Vanessa G. Felix, Apolinar Gonzalez, Walter Mata, Gladys E. Maestre


Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, still are unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively.  Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, is a useful tool for the detection and diagnosis of cardiac abnormalities.

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Hurxthal L.M. Clinical interpretation of the electrocardiogram. The New England Journal of Medicine, 1934, Vol. 211, No. 10, pp. 431-437.

Wolff A.R., Long S., McComb J.M., Richley D., Mercer P. The gap between training and provision: a primary-care based ECG survey in North-East England. The British Journal of Cardiology, 2012, Vol. 19, No. 1, pp. 38-40.

Richley D. New training and qualifications in electrocardiography. British Journal of Cardiac Nursing, 2013, Vol. 8, No. 1, pp. 38-42.

Som S. Nurse practitioners (and other physician extenders) are not an appropriate replacement for expert physician electrocardiogram readers in routine clinical practice. Journal of the American College of Cardiology, 2015, Vol. 65, No. 1, pp. 106-107.

Jambukia S.H., Dabhi V.K., Prajapati, H.B. Classification of ECG signals using machine learning techniques: A survey. Proc. of IEEE International Conference on Advances in Computer Engineering and Applications, 2015, pp. 714-721.

Javadi M., Arani S.A.A.A, Sajedin A., Ebrahimpour R. Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomedical Signal Processing and Control, 2013, Vol. 8, No. 3, pp. 289-296.

Zadeh A.E., Khazaee A., Ranaee V. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Computer Methods and Programs in Biomedicine, 2010, Vol. 99, No. 2, pp. 179-194.

Micó P., Mora M., Cuesta-Frau D., Aboy M. Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy. Computer Methods and Programs in Biomedicine, 2010, Vol. 98, No. 2, pp. 118-129.

Choudhary M., Narwaria R.P. Suppression of noise in ECG signal using low pass IIR filters. International Journal of Electronics and Computer Science Engineering, 2012, Vol. 1, No. 4, pp. 2238-2243.

Li Q., Clifford G.D. Signal quality and data fusion for false alarm reduction in the intensive care unit. Journal of Electrocardiology, 2012, Vol. 45, No. 6, pp. 596-603.

Güler, İ., Übeyli, E.D. (2005). ECG beat classifier designed by combined neural network model. Pattern recognition, 2005, Vol. 38, No. 2, pp. 199-208.

Vijayavanan M., Rathikarani V., Dhanalakshmi P. Automatic classification of ECG signal for heart disease diagnosis using morphological features. International Journal of Computer Science & Engineering Technology, 2014, Vol. 5, No. 4, pp. 449-455.

Naima, F.A, Timemy A.A. Neural network based classification of myocardial infarction: a comparative study of Wavelet and Fourier transforms. In Pattern Recognition, 2009, pp. 337-351. INTECH.

Setiawan I.M.A., Imah, E.M., Jatmiko, W. Arrhytmia classification using fuzzy-neuro generalized learning vector quantization. Proc. of IEEE International Conference on Advanced Computer Science and Information System, 2011, pp. 385-390.

Vishwa A.M.K., Lal S.D., Vardwaj P. Clasification of arrhythmic ECG data using machine learning techniques. International Journal of Interactive Multimedia and Artificial Intelligence, 2011, Vol. 1, No. 4 pp. 67-70.

Rohan M.D., Patil A.J. Layered approach for ECG beat classification utilizing neural network. Bioinformatics, 2012, Vol. 2, No. 6, pp. 1495-1500.

Asl B.M., Sharafat A.R., Setarehdan S.K. An adaptive backpropagation neural network for arrhythmia classification using RR interval signal. Neural Network World, 2012, Vol. 6, No. 12, pp. 535-54.

Das M.K., Ari S. ECG arrhythmia recognition using artificial neural network with S-transform based effective features. Proc. of IEEE India Conference Annual, 2013, pp. 1-6.

Tang X., Shu L. Classification of electrocardiogram signals with RS and quantum neural networks. International Journal of Multimedia and Ubiquitous Engineering, 2014, Vol 9, No. 2, pp. 363-372.

Li Q., Rajagopalan C., Clifford G.D. A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 2014, Vol. 117, No. 3, pp. 435-447.

Redmond S.J., Xie Y., Chang D., Basilakis J., Lovell N.H. Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiological Measurement, 2012, Vol. 33, No. 9, pp. 1517-1533.

Moody G.B., Mark R.G. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 2001, Vol. 20, No. 3, pp. 45-50.

Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.Ch., Mark R.G., Mietus J.E., Moody G.B., Peng, C.K., Stanley H.E. Physiobank, Physiotoolkit, and Physionet. Components of a new research resource for complex physiologic signals. Circulation, 2000, Vol. 101, No. 23, pp. e215-e220.

Garg P., Sharma A. Detection of normal ECG and arrhythmia using artificial neural network system. International Journal of Engineering Research and Science & Technology, 2015, Vol. 4, No. 1, pp. 1-13.

Moses D. A survey of data mining algorithms used in cardiovascular disease diagnosis from multi-lead ECG data. Kuwait Journal of Science, 2015, Vol. 42, No. 2, pp. 206-235.

Jadhav S., Nalbalwar S., Ghatol A. Performance evaluation of generalized feedforward neural network based ECG arrhythmia classifier. International Journal of Computer Science Issues, 2012, Vol. 9, No. 4, pp. 379-384

Ambrose M. ECG. Interpretación clínica. Manual Moderno, Buenos Aires, 2008.

Chierici F., Pignagnoli L., Embriaco D. Modeling of the hydroacoustic signal and tsunami wave generated by seafloor motion including a porous seabed. Journal of Geophysical Research, 2010. Vol. 115, No. C3, pp. 1-15.

Islam M.K., Haque A.N., Tangim G., Ahammad T., Khondokar M.R.H. Study and analysis of ECG signal using MATLAB & LABVIEW as effective tools. International Journal of Computer and Electric Engineering, 2012, Vol. 4, No. 3, pp. 404-408.

Guda M., Gasser S., El Mahallawy M.S. MATLAB simulation comparison for different adaptive noise cancelation algorithms. Proc. of the International Conference on Digital Information, Networking, and Wireless Communications, 2014, pp. 68-73.

Bille K., Figueiras D., Schamasch P., Kappenberger L., Brenner J.I., Meijboom F.J., Meijboom E.J. Sudden cardiac death in athletes: the Lausanne Recommendations. European Journal of Cardiovascular Prevention & Rehabilitation, 2006, Vol. 13, No. 6, pp. 859-875.

Cools E., Missant C. Junctional ectopic tachycardia after congenital heart surgery. Acta Anaesthesiologica Belgica, 2014, Vol. 65, No. 1, pp. 1-8.

Das M.K., Khan B., Jacob S., Kumar A., Mahenthiran J. Significance of a fragmented QRS complex versus a Q wave in patients with coronary artery disease. Circulation, 2006, Vol. 113, No. 21, pp. 2495-2501.

Valo M., Moller A., Teupe C. Markers of myocardial ischemia in patients with diabetes mellitus and severe obstructive sleep apnea impact of continuous positive airway pressure therapy. Journal of Diabetes & Metabolism, 2015, Vol. 6, No. 492, pp. 2-5.

Bakhoya V.N., Kurl S., Laukkanen J.A. T-wave inversion on electrocardiogram is related to the risk of acute coronary syndrome in the general population. European Journal of Preventive Cardiology, 2014, Vol. 21, No. 4, pp., 500-506.

Bousseljot R., Kreiseler D., Schnabel A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik/Biomedical Engineering, 1995, Vol. 40, No. s1, pp. 317-318.

Mainardi L., Sornmo L., Cerutti S. Understanding atrial fibrillation: the signal processing contribution. Morgan & Claypool Publishers, San Rafael, 2008.

Ramli A.B., Ahmad P.A. Correlation analysis for abnormal ECG signal features extraction. Proc. of 4th National Conference on Telecommunication Technology, 2003, pp. 232-237.

Szabó B.T., van der Vaart A.W., van Zanten J.H. Empirical Bayes scaling of Gaussian priors in the white noise model. Electronic Journal of Statistics, 2013, Vol. 7, pp. 991-1018.

Grandvalet Y., Canu S. Comments on noise injection into inputs in back propagation learning. IEEE Transactions Systems, Man and Cybernetics, 1995, Vol. 25, No. 4, pp. 678-681.

An G. The effects of adding noise during backpropagation training on a generalization performance. Neural Computation, 1996, Vol. 8, No. 3, pp. 643-674.

Kumar R., Gupta R., Jyoti K., Ranjan A.K. AT89C51 microcontroller based medical two channel ECG module and body temperature measurement with graphics LCD. International Journal of Advanced Research in Computer Science and Software Engineering, 2015, Vol. 5, No. 5, pp. 1321-1326.

Longini R.L., Giolma J.P., Wall III C., Quick R.F. Filtering without phase shift. IEEE Transactions on Biomedical Engineering, 1975, Vol. BME-22, No. 5, pp. 432-433.

Valverde E.R., Quinteiro R.A., Bertran G.C., Arini P.D., Glenny P., Biagetti M.O. Influence of filtering techniques on the time domain analysis of signal-averaged P wave electrocardiogram. Journal of Cardiovascular Electrophysiology, 1998, Vol. 9, No. 3, pp. 253-260.

Łęski J.M., Henzel N. ECG baseline wander and powerline interference reduction using nonlinear filter bank. Signal Processing, 2005, Vol. 85, No. 4, pp. 781-793.

Gregg R.E., Zhou S.H., Lindauer J.M., Helfenbein E.D., Giuliano K.K. What is inside the electrocardiograph?. Journal of Electrocardiology, 2008, Vol. 41, No. 1, pp. 8-14.

Berson A.S., Pipberger H.V. The low-frequency response of electrocardiographs, a frequent source of recording errors. American Heart Journal, 1966, Vol. 71, No. 6, pp. 779-789.

Bragg-Remschel D.A., Anderson C.M., Winkle R.A. Frequency response characteristics of ambulatory ECG monitoring systems and their implications for ST segment analysis. American Heart Journal, 1982, Vol. 103, No. 1, pp. 20-31.

Tayler D., Vincent R. Signal distortion in the electrocardiogram due to inadequate phase response. IEEE Transactions on Biomedical Engineering, 1983, Vol. BME-30, No. 6, pp. 352-356.

Tayler D.I, Vincent R. Artefactual ST segment abnormalities due to electrocardiograph design. British Heart Journal, 1985, Vol. 54, No. 2, pp. 121-128.

Burri H., Sunthorn H., Shah D. Simulation of anteroseptal myocardial infarction by electrocardiographic filters. Journal of Electrocardiology, 2006, Vol. 39, No. 3, pp. 253-258.

Censi F., Calcagnini G., Triventi M., Mattei E., Bartolini P., Corazza I., Boriani G. Effect of high-pass filtering on ECG signal on the analysis of patients prone to atrial fibrillation. Annali dell'Istituto Superiore di Sanità, 2009, Vol. 45, No. 4, pp. 427-431.

Buendía-Fuentes F., Arnau-Vives M.A., Arnau-Vives A., Jiménez-Jiménez Y., Rueda-Soriano J., Zorio-Grima E., Osa-Sáez A., Martínez-Dolz L.V., Almenar-Bonet L., Palencia-Pérez M.A. High-bandpass filters in electrocardiography: source of error in the interpretation of the ST segment. International Scholarly Research Notices Cardiology, 2012, Vol. 2012, pp. 1-10.

Li B., Tsao Y., Sim K.C. An investigation of spectral restoration algorithms for deep neural networks based noise robust speech recognition. Proc. of 14th Annual Conference of the International Speech Communication Association, 2013, pp. 3002-3006.

Yin S., Liu C., Zhang Z., Lin Y., Wang D., Tejedor J., Fang-Zhen T., Li Y. Noisy training for deep neural networks in speech recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2015, Vol. 2015, No. 1, pp. 1-14.


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