Abstract
The three main events presented in the electrocardiogram (ECG) signal of each heartbeat are: the P wave, the QRS complex and the T wave. Each event contains its own peak, making this important to analyze their morphology, amplitude and duration for cardiac abnormalities. In this study, we propose a system for biomedical signal analysis based on empirical mode decomposition. Mustispectral analysis is first performed to remove noise, detect QRS complex and compute the QRS wide. Then statistical features and QRS wide are after used as inputs of classifier based on neural network model. The proposed methodology is tested on real biomedical data and discussed.
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Al-Ashkar, T.: Medical multi signal signature recognition applied to cardiac diagnostic (2012)
Chang, P.C., Lin, J.J., Hsieh, J.C., Weng, J.: Myocardial infarcation classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl. Soft Comput. 12, 3165–3175 (2012)
Fatin, A.E., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T.: Arrhythmia recognition and classification using combined linear and nonlinear feature of ECG signals. Comput. Methods Programs Biomed. 127, 52–63 (2016)
Jin, F., Sugavaneswaran, L., Krishnan, S., Chauhan, V.S.: Quantification of fragmented QRS complex using intrinsic time-scale decomposition. Biomed. Signal Process. Control 31, 513–523 (2017)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEE Eng. Med. Biol. 20, 45–50 (2001)
Niang, O., Thioune, A., Delechelle, E., Lemoine, J.: Spectral intrinsic decomposition method for adaptative signal representation. ISRN Sig. Process. 9, 3 (2012)
Pal, S., Mitra, M.: Empirical mode decomposition based on ECG enhencement and QRS detection. Comput. Biol. Med. 42, 83–92 (2012)
Pasa, L., Testolin, A., Sperdui, A.: Neural networks for sequential data: a pretraining approach based on hidden Markov models. Neurocomputing 169, 323–333 (2015)
Polat, K., Gunes, S.: Detection of ECG arrythmia using a differential expert system approach based on principal component analysis and least square support vector machine. Appl. Math. Comput. 186, 898–906 (2007)
Ripoll, V.J.R., Wojdel, A., Romero, E., Ramos, P., Brugada, J.: ECG assessment based on neural networks with pretraining. Appl. Soft Comput. 49, 399–406 (2016)
Rodriguez, R., Mexicano, A., Bila, J., Cervantes, S., Ponce, R.: Feature extraction of electrocardiogram signals by aplying adaptative threshold and principal component analysis. J. Appl. Res. Technol. 13, 261–269 (2015)
Slimane, Z.H., Nait Ali, A.: QRS complex detection using empirical mode decomposition. Digit. Signal Proc. 20, 1221–1228 (2010)
Tjolleng, A., Jung, K., Hong, W., Lee, W., Lee, B., You, H., Son, J., Park, S.: Classification of a driver’s cognitive workload levels using artificial neural network on ECG signals. Appl. Ergon. 59, 326–332 (2017)
Tu, J.V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 45, 1225–1231 (1996)
Zhang, H.: An improved QRS wave group detection algorithm and matlab implementation. Phys. Procedia 25, 1010–1016 (2012)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Abdou, A.D., Ngom, N.F., Sidibé, S., Niang, O., Thioune, A., Ndiaye, C.H.T.C. (2018). Neural Networks for Biomedical Signals Classification Based on Empirical Mode Decomposition and Principal Component Analysis. In: M. F. Kebe, C., Gueye, A., Ndiaye, A. (eds) Innovation and Interdisciplinary Solutions for Underserved Areas. CNRIA InterSol 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-319-72965-7_25
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DOI: https://doi.org/10.1007/978-3-319-72965-7_25
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