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Chapitre D'ouvrage Année : 2020

A Neural Based Comparative Analysis for Feature Extraction from ECG Signals

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Résumé

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.
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Dates et versions

hal-03631436 , version 1 (05-04-2022)

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Giansalvo Cirrincione, Vincenzo Randazzo, Eros Pasero. A Neural Based Comparative Analysis for Feature Extraction from ECG Signals. Esposito, A and FaundezZanuy, M and Morabito, FC and Pasero, E. NEURAL APPROACHES TO DYNAMICS OF SIGNAL EXCHANGES, 151, pp.247-256, 2020, Smart Innovation, Systems and Technologies, 978-981-13-8950-4; 978-981-13-8949-8. ⟨10.1007/978-981-13-8950-4\_23⟩. ⟨hal-03631436⟩

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