Importantly, unlike previously developed algorithms, we propose a Support Vector Machine (SVM) based, IoT framework embedded scheme for diagnosing sleep apnea. SVM methods have …

In this project, we implemented a number of machine learning and deep learning methods for sleep apnea detection. Conventional machine learning methods have four main steps for sleep …

This study explored how a Support Vector Machine performed(SVM) compared to an ANN on this task. Polysomnography (PSG) is a sort of sleep study which produces the data that is used in …

Jan 9, 2020 · This data mining-driven study proposed a support vector machine (SVM)-based prediction model built with 2, 6, and 6 features commonly collected at clinic visits to identify …

A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while …

Two advanced models, Gradient Boosting Classifier and Quadratic Discriminant Analysis (QDA), are employed to classify sleep disorders into three categories: Healthy, Insomnia, and Sleep …

In this study, we performed simultaneous radar measurements and polysomnography on patients with sleep apnea. A support vector machine algorithm was applied to the radar data to …

This data mining-driven study proposed a support vector machine (SVM)-based prediction model built with 2, 6, and 6 features commonly collected at clinic visits to identify patients with apnea …

A support vector machine algorithm was applied to the radar data to automatically detect sleep apnea events. Support vector machine parameters were optimized using the relationship …

To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient …