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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© 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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