Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier

Recognition of Obstructive Sleep Apnea Syndrome

Palmprint recognition using HMAX model and Support Vector Machine classifier

Support vector machine (SVM) and HMAX model are two powerful recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper we combine these two techniques for the palmprint v...

Apert Syndrome with Obstructive Sleep Apnea: A Case Report

Apert syndrome is a rare kind of craniosynostosis which is identified with fusion of cranial sutures during prematurity period and causes physical and intellectual disorders in younger ages. These patients may show symptoms of obstructive sleep apnea due to abnormal craniofacial shape. This article introduces a patient with Apert syndrome, with apnea symptoms, cyanosis, snoring, restlessness, n...

[Obstructive sleep apnea syndrome].

This is an overview for the non-specialist of the pathophysiology, workup and treatment of the obstructive sleep apnea syndrome. The mechanisms leading to upper airway closure during sleep are discussed, as well as the current theoretical and experimental concepts for the apnea-induced arousal response and the consequent changes in sleep architecture and quality. Epidemiological data are summar...

Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine

To develop an applicable prediction for obstructive sleep apnea (OSA) is still a challenge in clinical practice. We apply a modern machine learning method, the support vector machine to establish a predicting model for the severity of OSA. The support vector machine was applied to build up a prediction model based on three anthropometric features (neck circumference, waist circumference, and bo...