A Critical Review of the Intelligent Computing Methods for the Identification of the Sleeping Disorders

Abstract

The intelligence computing techniques and the knowledge-centered systems are considered in the process of identifying the different complications in a clinical setting. In this article, a critical review of the different intelligent computing techniques, which are utilized in the detection of the sleeping disorders, will be analyzed. The core issue in this contribution is centered on the identification of the sleeping disorders such as snoring, parasomnia, insomnia, and sleep apnea. The mostly used diagnostic techniques by medical researchers are centered on the knowledge-based systems (KBSs), rule-based reasoning (RBR), the fuzzy logic (FL), case-based reasoning (CBR), artificial neural networks (ANNs), multi-layered perceptron (MLP), genetic algorithm (GA), neural networks (NNs), k-nearest neighbor (K-NN), data mining (DM), Bayesian network (BN), and the support vector machine (SVM), including other many methods integrated with the medical sector. As for the ancient methods, questionnaires are utilized for the identification of different forms of disorders, which have now been replaced with the above methods. This is meant to enhance sensitivity, specificity, and accuracy.