Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings


Because of the complexity, costs, and long wait times associated with sleep apnea-hypopnea syndrome (SAHS) diagnosis, a streamlined alternative is required. The blood oxygen saturation signal (SpO2) can be easily obtained through nocturnal oximetry and contains useful information about SAHS. In this work, 320 patients' SpO2 single-channel recordings were gathered at their residences and used to obtain statistical, spectral, nonlinear, and clinical SAHS-related information automatically. Relevant, nonredundant data from these investigations were then utilized to train and test four machine-learning techniques that can classify SpO2 signals into one of four degrees of SAHS severity (no-SAHS, mild, moderate, and severe). The diagnostic ability of the conventionally used 3 percent oxygen desaturation index was exceeded by all of the trained models (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multilayer perceptron, and AdaBoost). The best results came from an AdaBoost model that used linear discriminants as basis classifiers. In the SAHS severity classification, it achieved 0.479 Cohen's, as well as 92.9 percent, 87.4 percent, and 78.7 percent accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These findings imply that Machine Learning can be used in conjunction with SpO2 data collected at a patient's home to simplify SAHS diagnosis.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context ABSTRACT: Data has grown at an exponential rate as a result of recent developments in information technology, ushering
PROJECT TITLE :Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart GridABSTRACT:During this paper, a hierarchical good grid design is presented. The concept of good house is extended in 2 aspects: 1) from
PROJECT TITLE :Powered Two-Wheeler Riding Pattern Recognition Using a Machine-Learning FrameworkABSTRACT:During this paper, a machine-learning framework is employed for riding pattern recognition. The problem is formulated as
PROJECT TITLE : Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor Network - 2014 ABSTRACT: Recently, the research focus on geographic routing, a promising routing scheme in wireless sensor networks (WSNs),
PROJECT TITLE :T-Drive Enhancing Driving Directions with Taxi Drivers’ Intelligence - 2013ABSTRACT:This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry