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Chapitre d'ouvrage
A Neural Based Comparative Analysis for Feature Extraction from ECG Signals
Abstract : Automated ECG analysis and classification are nowadays a fundamental tool for monitoring patient heart activity properly. The most important features used in literature are the raw data of a time window, the temporal attributes and the frequency information from the eigenvector techniques. This paper compares these approaches from a topological point of view, by using linear and nonlinear projections and a neural network for assessing the corresponding classification quality. The nonlinearity of the feature data manifold carries most of the QRS-complex information. Indeed, it yields high rates of classification with the smallest number of features. This is most evident if temporal features are used: Nonlinear dimensionality reduction techniques allow a very large data compression at the expense of a slight loss of accuracy. It can be an advantage in applications where the computing time is a critical factor. If, instead, the classification is performed offline, the raw data technique is the best one.
https://hal-u-picardie.archives-ouvertes.fr/hal-03631436
Contributeur : Louise Dessaivre Connectez-vous pour contacter le contributeur
Soumis le : mardi 5 avril 2022 - 16:25:11
Dernière modification le : mercredi 6 avril 2022 - 03:00:16
Contributeur : Louise Dessaivre Connectez-vous pour contacter le contributeur
Soumis le : mardi 5 avril 2022 - 16:25:11
Dernière modification le : mercredi 6 avril 2022 - 03:00:16