Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data
This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps – logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.
KeywordsECG Deterministic chaos Artificial Neural Networks
Unable to display preview. Download preview PDF.
- 3.Guzzetti, S., Signorini, M.G., Cogliati, C., Mezzetti, S., Porta, A., Cerutti, S., Malliani, A.: Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and heart-transplanted patients. Cardiovascular 6363(95), 441–446 (1996)Google Scholar
- 5.Ho, K.K.L., Moody, G.B., Peng, C.K., Mietus, J.E., Larson, M.G., Levy, D., Goldberger, A.L.: Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96(3), 842–848 (1997)CrossRefGoogle Scholar
- 15.Losada, R.: ECG. 1988-2002 The MathWorks, Inc.Google Scholar
- 17.Goldberger, L.A., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Resource for Complex Physiologic Signals PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research. Circulation 101(23), e215–e220 (2000)Google Scholar