Objective An innovative method based on topological data analysis is introduced for
classifying EEG recordings of patients affected by epilepsy. We construct a topological …

In this paper we present a novel methodology based on a topological entropy, the so-called
persistent entropy, for addressing the comparison between discrete piecewise linear …

We develop a method for analyzing multivariate time series using topological data analysis
(TDA) methods. The proposed methodology involves converting the multivariate time series …

Topology is the branch of mathematics that studies shapes and maps among them. From the
algebraic definition of topology a new set of algorithms have been derived. These algorithms …

Every moment of our daily life belongs to the new era of" Big Data". We continuously
produce, at an unpredictable rate, a huge amount of heterogeneous and distributed data …

In some applications, it is important to compare the stochastic properties of two multivariate
time series that have unequal dimensions. A new method is proposed to compare the …

We develop a framework for analyzing multivariate time series using topological data
analysis (TDA) methods. The proposed methodology involves converting the multivariate …

In this work we study how to apply topological data analysis to create a method suitable to
classify EEGs of patients affected by epilepsy. The topological space constructed from the …

Distinguishing between classes of time series sampled from dynamic systems is a common
challenge in systems and control engineering, for example in the context of health …

In this paper we present a novel methodology based on a topological entropy, the so-called
persistent entropy, for addressing the comparison between discrete piece-wise linear …