A neuro-fuzzy approach using Hermite characterization of QRS complexes demonstrated very good performance in the recognition and classification of heart rhythms from ECG waveforms.
A neuro-fuzzy network using Hermite polynomials effectively recognizes and classifies heart rhythms from ECG waveforms.
This paper presents a neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms. The important part in recognition fulfills the Hermite characterization of the QRS complexes. The Hermite coefficients serve as the features of the process. These features are applied to a fuzzy neural network for recognition. The results of numerical experiments have confirmed very good performance of such a solution.
Linh et al. (2003) studied Heart rhythm classification. Neuro-fuzzy approach with Hermite characterization of QRS complexes was evaluated on Recognition and classification of heart rhythms. A neuro-fuzzy approach using Hermite characterization of QRS complexes demonstrated very good performance in the recognition and classification of heart rhythms from ECG waveforms.