Support vector machines for detection of electrocardiographic changes in partial epileptic patients

The aim of this study is to evaluate the diagnostic accuracy of the support vector machines (SVMs) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. Post-ictal heart rate oscillations were studied in a heterogeneous group of patients with partial epilepsy. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem, and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features, which well represent the ECG signals, and the SVMs trained on these features achieved high classification accuracies (total classification accuracy was 99.44%).

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

[2]  Elif Derya Übeyli,et al.  Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks , 2004, Eng. Appl. Artif. Intell..

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

[5]  Stanislaw Osowski,et al.  Support vector machine-based expert system for reliable heartbeat recognition , 2004, IEEE Transactions on Biomedical Engineering.

[6]  Elif Derya Übeyli,et al.  An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet‐based neural networks , 2005, Expert Syst. J. Knowl. Eng..

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

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

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Sarabjeet Singh Mehta,et al.  Development of entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram , 2007, Signal Process..

[11]  Jaques Reifman,et al.  Application of Information Technology: A Method for Automatic Identification of Reliable Heart Rates Calculated from ECG and PPG Waveforms , 2006, J. Am. Medical Informatics Assoc..

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

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

[14]  Kai Liu,et al.  A novel large-memory neural network as an aid in medical diagnosis applications , 2001, IEEE Transactions on Information Technology in Biomedicine.

[15]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

[16]  Elif Derya Übeyli.  Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats , 2008, Digit. Signal Process..

[17]  Nurettin Acir.  A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems , 2006, Expert Syst. Appl..

[18]  Jeffrey M. Hausdorff,et al.  Postictal heart rate oscillations in partial epilepsy. , 1999, Neurology.

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

[20]  Elif Derya Übeyli.  ECG beats classification using multiclass support vector machines with error correcting output codes , 2007, Digit. Signal Process..

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