Application of Koniocortex-Like Networks to Cardiac Arrhythmias Classification

Abstract

KLN (Koniocortex Like Network) is a novel Bioinspired Artificial Neural 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 showing several 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 of the 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.