000163961 001__ 163961
000163961 005__ 20251113160752.0
000163961 0247_ $$2doi$$a10.1016/j.neucom.2025.131820
000163961 0248_ $$2sideral$$a146121
000163961 037__ $$aART-2025-146121
000163961 041__ $$aeng
000163961 100__ $$0(orcid)0000-0002-3431-0926$$aMayora-Cebollero, Carmen$$uUniversidad de Zaragoza
000163961 245__ $$aDeep Learning for analyzing chaotic dynamics in biological time series: Insights from frog heart signals
000163961 260__ $$c2025
000163961 5060_ $$aAccess copy available to the general public$$fUnrestricted
000163961 5203_ $$aThe study of experimental data is a relevant task in several physical, chemical and biological applications. In particular, the analysis of chaotic dynamics in cardiac systems is crucial as it can be related to certain pathological arrhythmias. When working with short and noisy experimental time series, some standard techniques for chaos detection cannot provide reliable results because of such data characteristics. Moreover, when small datasets are available, some Deep Learning techniques cannot be applied directly (that is, using part of the data to train the network, and using the trained network to analyze the remaining dataset). To overcome all these limitations, we propose an automatic algorithm that combines Deep Learning and some selection strategies based on a mathematical model of the same nature of the experimental data. To demonstrate its performance, we test it with experimental data obtained from ex-vivo frog heart experiments, achieving highly accurate results.
000163961 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-122961NB-I00$$9info:eu-repo/grantAgreement/ES/DGA/E24-23R$$9info:eu-repo/grantAgreement/ES/DGA/LMP94_21$$9info:eu-repo/grantAgreement/ES/MCINN/PID2024-156032NB-I00$$9info:eu-repo/grantAgreement/ES/MCIU/FPU20-04039$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-140556OB-I00
000163961 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000163961 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000163961 700__ $$aFenton, Flavio H.
000163961 700__ $$aHalprin, Molly
000163961 700__ $$aHerndon, Conner
000163961 700__ $$aToye, Mikael J.
000163961 700__ $$0(orcid)0000-0002-8089-343X$$aBarrio, Roberto$$uUniversidad de Zaragoza
000163961 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000163961 773__ $$g660 (2025), 131820 [14 pp.]$$pNeurocomputing$$tNeurocomputing$$x0925-2312
000163961 8564_ $$s5485577$$uhttps://zaguan.unizar.es/record/163961/files/texto_completo.pdf$$yVersión publicada
000163961 8564_ $$s2534996$$uhttps://zaguan.unizar.es/record/163961/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000163961 909CO $$ooai:zaguan.unizar.es:163961$$particulos$$pdriver
000163961 951__ $$a2025-11-13-14:58:38
000163961 980__ $$aARTICLE