Self-Organizing Maps and its Applications in Sleep Apnea Research and Molecular Genetics

Abstract

This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical levels enables the discovery of complex structures as present in this type of applications. Furthermore, an integration of unsupervised neural networks with hidden markov models is proposed.

Spatial Clustering Using Hierarchical SOM

“…An example is the work of [58,60], where the authors use HSOM to perform sequence classification and discrimination in musical and electric power load data. Another example is [62] where HSOM is used to process sleep apnea data.…”