Detection of obstructive sleep apnea through ECG signal features

Abstract

Abstract-Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.

A Panoramic Study of Fall Detection Technologies

“…Thus, to enhance the utility of this literature, we are planning to develop a comprehensive automated health monitoring system, that fall detection using PIR sensor as one component in conjunction with our previous published works such as heart beats and arterial oxygen saturation monitoring [14,15], in addition to other unpublished work which aims to help blind person to detect obstacle using ultrasonic sensor and Arduino microcontroller. Therefore, in this regard, we are planning to use our previous works as a core along with new proposed fall detection model to build a multi parameters system and apply that as a system for automated health monitoring system.…”

“…In our previous published researches [14] [15], we studied the Electrocardiogram (ECG) signal and Arterial oxygen saturation (SpO 2 ) and we developed an automated classification model can recognize irregular heartbeats and SpO 2 variability. Therefore, in this regard, we are planning to use our previous works as a core along with new proposed fall detection model to build a multi parameters system and apply that as a system for automated health monitoring system.…”

A Novel Technique to Improve ECG Signal using SVM

“…[11]"Apnea Med Assist" Device for diagnosing obstructive sleep apnea which became applied on smart telephones based totally android platform imparting approx 96%sensitivity. [12] An automated classification algorithm which determines events of short duration of ECG data. This algorithm used SVM as a classifier for apnea affected information functions classification which becomes taken from Physionet Apnea ECG signals database providing accuracy of approx 96.5%.…”

Sleep apnea detection using time-delayed heart rate variability

“…SVM-based classifiers have been widely used for SA detection [11] [12] [14]. SVM is a machine learning method allowing for obtaining the optimal boundary of two data sets or classes which are non-linearly mapped in a high-dimension feature space, independently on the probabilistic distributions of training vectors in the data sets.…”

“…In addition, due to the existence of individual differences in cardiac pathophysiology for SA patients, we expected that using a subject-specific approach would result in a better SA detection performance than a subject-independent approach. A support vector machine-(SVM-) based classifier, which has been successfully used in previous studies [11] [12] [14], was employed in this work as well. Figure 1.…”