2025/09/16 by Carmen Mayora-Cebollero, Flavio H. Fenton, Mayora-Cebollero, Carmen +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Fractal and DNA sequence analysis #Neural Networks and Applications #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2509.13027
openalex publication_date 2025/09/16 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28
The 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 some 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, 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 avoid 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 show its performance, we test it with experimental data obtained from ex-vivo frog heart experiments, obtaining highly accurate results.