RT Journal Article
T1 Identifying HRV patterns in ECG signals as early markers of dementia
A1 Arco, Juan E.
A1 Gallego-Molina, Nicolás J.
A1 Ortiz-García, Andrés
A1 Arroyo-Alvis, Katy
A1 López-Pérez, P. Javier
K1 Electrocardiografía
K1 Demencia
K1 Procesado de señales
AB The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. Thesetools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screeninganalysis and disentangle the information that they contain. We propose in this work a novel method forevaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers acomplete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to thefinal classification stage. Numerous metrics are computed from the original data in terms of different domains(time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). Theresulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines,Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an areaunder the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI)patients, showing that ECG contain crucial information for predicting the appearance of this pathology. Theseresults are specially relevant given the fact that ECG acquisition is much more affordable and less invasivethan brain imaging used in most of these intelligent systems, allowing our method to be used in environmentsof any socioeconomic range.
PB Elsevier
YR 2023
FD 2023-12-15
LK https://hdl.handle.net/10630/28866
UL https://hdl.handle.net/10630/28866
LA eng
NO Juan E. Arco, Nicolás J. Gallego-Molina, Andrés Ortiz, Katy Arroyo-Alvis, P. Javier López-Pérez, Identifying HRV patterns in ECG signals as early markers of dementia, Expert Systems with Applications, Volume 243, 2024, 122934, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.122934. (https://www.sciencedirect.com/science/article/pii/S095741742303436X)
NO Funding for open access charge: Universidad de Málaga/CBUA. This work was supported by projects PID2022-137461NB-C32 (Spanish “Ministerio de Ciencia e Innovación, /AEI /10.13039/501100011033/ FEDER, UE), UMA20-FEDERJA-086 European Regional Development Funds (ERDF) “Una manera de hacer Europa”, and by Spanish “Ministerio de Universidades” (Next Generation EU funds) through Margarita-Salas grant to J.E. Arco.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026