An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet‐based neural networks

: In this study a wavelet-based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta-bar-delta, extended delta-bar-delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT-BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.

[1]  S. T. Hamde,et al.  Feature extraction from ECG signals using wavelet transforms for disease diagnostics , 2002, Int. J. Syst. Sci..

[2]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[3]  T. Tomson,et al.  Heart rate variability in patients with epilepsy 1 Presented in part at the 2nd European Congress of Epileptology, The Hague, September, 1996. 1 , 1998, Epilepsy Research.

[4]  Michael G. Strintzis,et al.  ECG pattern recognition and classification using non-linear transformations and neural networks: A review , 1998, Int. J. Medical Informatics.

[5]  C. Baumgartner,et al.  Electrocardiographic Changes at the Onset of Epileptic Seizures , 2003, Epilepsia.

[6]  Metin Akay,et al.  Wavelet applications in medicine , 1997 .

[7]  Ali A. Minai,et al.  Back-propagation heuristics: a study of the extended delta-bar-delta algorithm , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[8]  Elif Derya Übeyli,et al.  Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network , 2003, Comput. Biol. Medicine.

[9]  Yu Zhang,et al.  Doppler ultrasound signal denoising based on wavelet frames. , 2001, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[10]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[11]  B. H. Blott,et al.  Review of neural network applications in medical imaging and signal processing , 1992, Medical and Biological Engineering and Computing.

[12]  L. Hirsch,et al.  Heart rate and EKG changes in 102 seizures: analysis of influencing factors , 2002, Epilepsy Research.

[13]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[14]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[15]  C. Elger,et al.  Cardiac Asystole in Epilepsy: Clinical and Neurophysiologic Features , 2003, Epilepsia.

[16]  B P Simon,et al.  An ECG classifier designed using modified decision based neural networks. , 1997, Computers and biomedical research, an international journal.

[17]  Mahantapas Kundu,et al.  Knowledge-based ECG interpretation: a critical review , 2000, Pattern Recognit..

[18]  M. Akay,et al.  Wavelets for biomedical signal processing , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[19]  J. Gotman,et al.  Heart Rate Changes and ECG Abnormalities During Epileptic Seizures: Prevalence and Definition of an Objective Clinical Sign , 2002, Epilepsia.

[20]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[21]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[22]  B. A. Harvey,et al.  Neural network-based EKG pattern recognition , 2002 .

[23]  Karsten Sternickel,et al.  Automatic pattern recognition in ECG time series , 2002, Comput. Methods Programs Biomed..

[24]  J. N. Watson,et al.  Evaluating arrhythmias in ECG signals using wavelet transforms , 2000, IEEE Engineering in Medicine and Biology Magazine.

[25]  Chris D. Nugent,et al.  An intelligent framework for the classification of the 12-lead ECG , 1999, Artif. Intell. Medicine.

[26]  Tamer Ölmez,et al.  ECG beat classification by a novel hybrid neural network , 2001, Comput. Methods Programs Biomed..

[27]  William G. Baxt,et al.  Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion , 1990, Neural Computation.

[28]  Elif Derya Übeyli,et al.  Neural network analysis of internal carotid arterial Doppler signals: predictions of stenosis and occlusion , 2003, Expert Syst. Appl..