Laiali Almazaydeh

Detection of obstructive sleep apnea through ECG signal features

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.

Obstructive sleep apnea detection using SVM-based classification of ECG signal features

Sleep apnea is the instance when one either has pauses of breathing in their sleep, or has very low breath while asleep. This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PSG) at sleep labs. PSG is both expensive and inconvenient as an expert human observer is required to work over night. New sleep apnea classification techniques are nowadays being developed by bioengineers for most comfortable and timely detection. This paper focuses on an automated classification algorithm which processes short duration epochs of the electrocardiogram (ECG) data. The presented classification technique 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 accuracy of 96.5% or higher. Furthermore, the proposed system can be used as a basis for future development of a tool for OSA screening.

A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features

Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. These episodes last 10 seconds or more and cause oxygen levels in the blood to drop. 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 required. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this study, we develop and validate a neural network (NN) using SpO2 measurements obtained from pulse oximetry to predict OSA. The results show that the NN is useful as a predictive tool for OSA with a high performance and improved accuracy, approximately 93.3%, which is better than reported techniques in the literature.

Apnea Detection based on Respiratory Signal Classification

Abstract Obstructive sleep apnea (OSA) is the most common form of different types of sleep-related breathing disorders. It is characterized by repetitive cessations of respiratory flow during sleep, which occurs due to a collapse of the upper respiratory airway. OSA is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying the presence of sleep apnea based on the acoustic signal of respiration. The characterization of breathing sound was carried by Voice Activity Detection (VAD) algorithm, which is used to measure the energy of the acoustic respiratory signal during breath and breath hold. The performance of our classification algorithm is tested on real respiratory signals and the experimental results show that the VAD is useful as a predictive tool for the segmentation of breath into sound and silence segments. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.

A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection

Sleep apnea (SA) in the form of Obstructive sleep apnea (OSA) is becoming the most common respiratory disorder during sleep, which is characterized by cessations of airflow to the lungs. These cessations in breathing must last more than 10 seconds to be considered an apnea event. Apnea events may occur 5 to 30 times an hour and may occur up to four hundred times per night in those with severe SA [1]. Nowadays, polysomnography (PSG) is a standard testing procedure to diagnose OSA which includes the monitoring of the breath airflow, respiratory movement, and oxygen saturation (SpO 2 ), body position, electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG). Therefore, a final diagnosis decision is obtained by means of medical examination of these recordings [2]. However, new simplified diagnostic methods and continuous screening of OSA is needed in order to have a major benefit of the treatment on OSA outcomes. In this regard, a portable monitoring system is developed to facilitate the self-administered sleep tests in familiar surroundings environment closer to the patients’ normal sleep habits. With only three data channels: tracheal breathing sounds, ECG and SpO 2 signals, a patient does not need hospitalization and can be diagnosed and receive feedback at home, which eases follow-up and retesting after treatment.

Designing and Integrating a New Model of Semi-Online Vehicle’s Fines Control System

In this paper we suggest to develop a vehicle’s speed and fines control system to manage and control different aspects of fleet and cruise management system. The system developed to be sponsored by the government, which is represented by Department of Motor Vehicle (DMV) and should be operated by them. The purposes of the proposed project include speed, passengers’ safety and vehicle readiness and related fines associated with driving practice such as wearing seat belt and speed limits. The system can be implemented by developing a software system inside a chip supported by recent related technologies such as GPS, GPRS and cameras, then installing the chip into the vehicle. The final outcome will be levying penalties respective to the driver’s mistakes and offences; in addition new era of communication between DMV and driver, vehicle and driver, driver and DMV will be followed.