Importantly, unlike previously developed algorithms, we propose a Support Vector Machine (SVM) based, IoT framework embedded scheme for diagnosing sleep apnea. SVM methods have become a standard means for data classification primarily due to their simplistic numerical comparisons and optimal solution determination ease for a specific context ...

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 apnea detection: pre-processing, feature extraction, feature selection, and classification.

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 classifying sleep disorders.

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 patients with apnea-hypopnea index (AHI) ≥5/h, ≥15/h, and ≥30/h, respectively.

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 the oxygen saturation gave the highest specificity.

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

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 automatically detect sleep apnea events. Support vector machine parameters were optimized using the relationship between the radar and polysomnography data.

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-hypopnea index (AHI) ≥5/h, ≥15/h, and ≥30/h, respectively.

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 between the radar and polysomnography data.

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 Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide ...