Comparative evaluation of probabilistic neural network versus support vector machines classifiers in discriminating ERP signals of depressive patients from healthy controls

This paper describes the design of classification system capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised twenty-five patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEC activity was recorded from 15 scalp electrodes and recordings were digitized for further computer processing. Features related to the shape of the waveform were generated using a dedicated custom software interface system developed in C++ for the purposes of this work. A software classification system was designed, consisting of (a) two classifiers, the probabilistic neural network (PNN) and the support vector machines (SVM), (b) two routines for feature reduction and feature selection, and (c) an overall system evaluation routine, comprising the exhaustive search and the leave-one-out methods. Highest classification accuracies achieved were 92% for the PNN and 96% for the SVM, using the 'latency/amplitude ratio' and 'peak-to-peak slope' two-feature combination. In conclusion, employing computer-based pattern recognition techniques with features not easily evaluated by the clinician, patients with depression could be distinguished from healthy subjects with high accuracy.

[1]  J. Slater,et al.  Neural network analysis of the P300 event-related potential in multiple sclerosis. , 1994, Electroencephalography and clinical neurophysiology.

[2]  Ah Chung Tsoi,et al.  Classification of Electroencephalogram Using Artificial Neural Networks , 1993, NIPS.

[3]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[4]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[5]  Raveendran Paramesran,et al.  Using genetic algorithm to identify the discriminatory subset of multi-channel spectral bands for visual response , 2002, Appl. Soft Comput..

[6]  P. Tueting,et al.  Auditory evoked potentials, clinical vs. research applications , 1997, Psychiatry Research.

[7]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[8]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[9]  Timothy J. Dasey,et al.  Detection of multiple sclerosis with visual evoked potentials - an unsupervised computational intelligence system , 2000, IEEE Transactions on Information Technology in Biomedicine.

[10]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[11]  C. Stefanis,et al.  Changes of P300 Elicited during a Working Memory Test in Individuals with Depersonalization-Derealization Experiences , 2002, Neuropsychobiology.

[12]  Guowang Xu,et al.  Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data , 2002 .

[13]  C. Papageorgiou,et al.  A Decision Support System of Evoked Potentials for the Classification of Patients with First-Episode Schizophrenia , 2003 .

[14]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[15]  J. Binder,et al.  Functional Magnetic Resonance Imaging , 1997 .

[16]  M Hajmeer,et al.  A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. , 2002, Journal of microbiological methods.

[17]  J R Sveinsson,et al.  Parallel principal component neural networks for classification of event-related potential waveforms. , 1997, Medical engineering & physics.