2026-04-17T22:51:10Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2180422025-12-05T09:55:37Zcom_2072_1057col_2072_478917col_2072_478920

00925njm 22002777a 4500 dc Ferrà Marcús, Aina author Casacuberta, Carles author Pujol Vila, Oriol author 2025-01-28T09:01:55Z 2025-01-28T09:01:55Z 2023-07-19 2025-01-28T09:01:56Z This 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%. Xarxes neuronals (Informàtica) Anàlisi de sèries temporals Homologia Neural networks (Computer science) Time-series analysis Homology Importance attribution in neural networks by means of persistence landscapes of time series