%0 Book Section %T A Neural Based Comparative Analysis for Feature Extraction from ECG Signals %+ Laboratoire des technologies innovantes - UR UPJV 3899 (LTI) %+ Politechnico di Torino %+ Department of Electronics and Telecommunications [Torino] (DET) %A Cirrincione, Giansalvo %A Randazzo, Vincenzo %A Pasero, Eros %@ 978-981-13-8950-4; 978-981-13-8949-8 %B NEURAL APPROACHES TO DYNAMICS OF SIGNAL EXCHANGES %E Esposito %E A and FaundezZanuy %E M and Morabito %E FC and Pasero %E E %S Smart Innovation, Systems and Technologies %V 151 %P 247-256 %8 2020 %D 2020 %R 10.1007/978-981-13-8950-4\_23 %Z Engineering Sciences [physics]Book sections %X 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. %G English %L hal-03631436 %U https://u-picardie.hal.science/hal-03631436 %~ UNIV-PICARDIE %~ U-PICARDIE %~ LTI