Doppler ultrasound signals analysis using multiclass support vector machines with error correcting output codes

In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for the multiclass Doppler ultrasound signals (ophthalmic arterial Doppler signals and internal carotid arterial Doppler signals) classification problems. 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 studied Doppler ultrasound signals and the multiclass SVMs trained on these features achieved high classification accuracies.