Ferrà Marcús, AinaCasacuberta, CarlesPujol Vila, Oriol2025-01-282025-01-282023-07-190941-0643https://hdl.handle.net/2445/218042This article describes a method to analyze time series with a neural network using a matrix of area-normalized persistence landscapes obtained with topological data analysis. The network’s architecture includes a gating layer that is able to identify the most relevant landscape levels for a classification task, thus working as an importance attribution system. Next, a matching is performed between the selected landscape levels and the corresponding critical points of the original time series. This matching enables reconstruction of a simplified shape of the time series that gives insight into the grounds of the classification decision. As a use case, this technique is tested in the article with input data from a dataset of electrocardiographic signals. The classification accuracy obtained using only a selection of landscape levels from data was 94.00% averaged after five runs of a neural network, while the original signals achieved 98.41% and landscape-reduced signals yielded 97.04%.14 p.application/pdfengcc by (c) Aina Ferrà Marcús et al., 2023http://creativecommons.org/licenses/by/3.0/es/Xarxes neuronals (Informàtica)Anàlisi de sèries temporalsHomologiaNeural networks (Computer science)Time-series analysisHomologyinfo:eu-repo/semantics/article7339442025-01-28info:eu-repo/semantics/openAccess