Application of koniocortex-like networks to cardiac arrhythmias classification

• Autores: Santiago Torres, Yasmine Benchaib, José Manuel Ferrández Vicente, Diego Andina de la Fuente
• Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 264-273
• Idioma: inglés
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• Resumen

  • KLN (Koniocortex Like Network) is a novel Bioinspired ArtificialNeural Network that models relevant biological properties of neurons as Synaptic Directionality, Long Term Potenciation, Long Term Depression, Metaplasticity and Intrinsic plasticity, together with natural normalization of sensory inputs and Winner-Take-All competitive learning.As a result, KLN performs a Deeper Learning on DataSets showingseveral high order properties of biological brains as: associative memory, scalability and even continuous learning. KLN learning is originally unsupervised and its architecture is inspired in the koniocortex, the first cortical layer receiving sensory inputs where map reorganization and feature extraction have been identified, as is the case of the visual cortex.This new model has shown big potential on synthetic inputs and research is now on application performance in complex problems involving real data in comparison with state-of-art supervised and unsupervised techniques.In this paper we apply KLN to explore its capabilities on one ofthe biggest problem of nowadays society and medical community, as it is the early detection of cardiovascular disease. The world’s number one killer, with 17,9 million deaths every year. Results of KLN on the classification of Cardiac arrhythmias from the well-known MIT-BIH cardiac arrhythmias database are reported