Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents

In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for the multiclass time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electrocardiogram signals) classification problems. Decision making was performed in two stages: feature extraction by computing the Lyapunov exponents 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 research demonstrated that the Lyapunov exponents are the features which well represent the studied time-varying biomedical signals and the multiclass SVMs trained on these features achieved high classification accuracies.