An interactive, real-time, high precision and portable monitoring system of obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common type of sleep apnea which is defined as the suspension of breathing. OSA is generally caused by complete or partial obstruction of airway during sleep, making the breathing pattern irregular and abnormal for prolonged periods of time. Apnea can contribute to a variety of life threatening medical conditions, and can be deadly if left untreated. Nowadays, out of 18 to 50 million people in the US, most cases remain undiagnosed due to the cost, cumbersome and resource limitations of overnight polysomnography (PSG) at sleep labs. Currently PSG relies on a doctor's experience. In order to improve the medical service efficiency, reduce diagnosis time and ensure a more accurate diagnosis, a quantitative and objective method is needed. In this dissertation, an innovative method in characterizing bio-signals for detecting epochs of sleep apnea with high accuracy is presented. Three data channels that are related to breath defect; respiratory sound, ECG and SpO2 are investigated, in order to extract physiological indicators that characterize sleep apnea. An automated method was used to analyze the respiratory sound to find pauses in breathing. Furthermore, the automated method analyzed ECG to find irregular heartbeats and SpO 2 to find rises and drops. The system consists of three main parts which are signal segmentation, features extraction and features classification. Feature extractions process is based on statistical measures. Features classification process is learned through Support Vector Machines (SVMs) and Neural Network (NN) classifiers. Moreover, a preprocessing technique is carried out to distinguish the R-wave from the other waves of the ECG signal. The approach presented in this dissertation was tested using downloaded polysomnographic ECG and SpO2 data from the Physionet database. In addition, to identifying sleep apnea using the acoustic signal of respiration; the characterization of breathing sound was carried by Voice Activity Detection (VAD) algorithm. VAD was used to measure the energy of the acoustic respiratory signal during breath and silence segments. From the experimental results for the three signals, it was concluded that the precision of classifying sleep apnea has an accuracy of 97%. This result offers a clinical reference value for identifying OSA instead of expensive PSG visual scoring method which is commonly used to asses sleep apnea, and could reduce diagnostic time and improve medical service efficiency

Parallel Processing for Multi Face Detection and Recognition

In this paper, a robust approach for real time face recognition where the images come from live video is proposed. To improve the algorithmic efficiency of face detection, we combine the eigenface method using Haar-like features to detect both of eyes and face, and Robert cross edge detector to locate the human face position. Robert Cross uses the integral image representation and simple rectangular features to eliminate the need of expensive calculation of multi-scale image pyramid. Moreover, In order to provide fast response in our system, we use Principal Component Analysis (PCA) to reduce the dimensionality of the training set, leaving only those features that are critical for face recognition. Eigendistance is used in face recognition to match the new face while it is projected on the face space. The matching is done when the variation difference between the new image and the stored image is below the threshold value. The experimental results demonstrate that the proposed scheme significantly improves the recognition performance. Overall, we find the system outperforms other techniques. Moreover, the proposed system can be used in different vision-based human computer interaction such as ATM, cell phone, intelligent buildings, etc

Development of OSA Event Detection Using Threshold Based Automatic Classification

Obstructive Sleep Apnea (OSA) is a very serious sleeping disorder resulting in the temporary blockage of the airflow airway that can be deadly if left untreated. OSA is not a rare condition; in the US, from 18 to 50 million people, most of them remain undiagnosed due to cost, cumbersome and resource limitations of overnight polysomnography (PSG) at sleep labs. Instead, automated, at-home devices that patients can simply use while asleep seem to be very attractive and highly on-demand. This paper presents a method for OSA screening and user notification based on the respiratory recording and video monitoring as a secondary system during sleep in order to alert of the apnea event and help patient to recover

A novel defect detection method for software requirements inspections

The requirements form the basis for all software products. Apparently, the requirements are imprecisely stated when scattered between development teams. Therefore, software applications released with some bugs, missing functionalities, or loosely implemented requirements. In literature, a limited number of related works have been developed as a tool for software requirements inspections. This paper presents a methodology to verify that the system design fulfilled all functional requirements. The proposed approach contains three phases: requirements collection, facts collection, and matching algorithm. The feedback results provided enable analysist and developer to make a decision about the initial application release while taking on consideration missing requirements or over-designed requirements

A Panoramic Study of Obstructive Sleep Apnea Detection Technologies

This study offers a literature research reference value for bioengineers and practitioner medical doctors. It could reduce research time and improve medical service efficiency regarding Obstructive Sleep Apnea (OSA) detection systems. Much of the past and the current apnea research, the vital signals features and parameters of the SA automatic detection are introduced.The applications for the earlier proposed systems and the related work on real-time and continuous monitoring of OSA and the analysis is given. The study concludes with an assessment of the current technologies highlighting their weaknesses and strengths which can set a roadmap for researchers and clinicians in this rapidly developing field of study

A New Model for Diagnosing Sleep Apnea through Features Extraction of the SpO2 Signal

Obstructive Sleep Apnea (OSA), is thenmost common form of different types of sleep-related breathing disorders. It is characterized by repetitive cessations of respiratory flow during sleep, which occur due to a collapse of the upper airway at the level of the oropharynx. The traditional diagnosis of OSA requires an expensive and complex overnight procedure called polysomnography (PSG). PSG contains several biomedical signals recording set, such as EEG, EOG, EMG, ECG, respiration and SpO2. In contrast,simple monitoring systems can be built as cheaper alternatives to the current PSGs in the diagnosis of OSA, which can also reduce the abundant burdens of hospital sleep centers.In this study, we develop a comprehensive feature set based on the arterial oxygen saturation signal measured by pulse oximetry (SpO2) to obtain high quality signal features in discriminating the OSA. The three features of Spo2 signal which are Delta Index, Central Tendency Measure with radius 0.5 (ctm50) and Oxygen Desaturation Index(odi3) are extracted, tested and evaluated using the MATLAB toolset. It was found that SpO2 signal characteristics could be helpful in order to evaluate sleep quality

SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.http://dx.doi.org/10.18201/ijisae.7907

Performance Model for a Conservative Distributed Simulation Environment Using Null Messages to Avoid Deadlock

A conservative distributed simulation requires all logical processes (LPs) to follow the causality constraint requirement. This implies that all event-messages are processed in strictly timestamp order. Apart from the timestamp of each event generated by LPs, synchronization between all LPs is the second most important requirements. Finally, there must not be a deadlock in the distributed environment. A deadlock may occur when there is no events present in the queue of LP. In such case, to avoid deadlock, Chandy-Misra-Bryant presented an algorithm called Null Message Algorithm (NMA) [3]. These null messages are passed as an event-message to other LPs and it stored in one of queues of LPs. This null message indicates that till the time stamp of that null message, all other events in the queue which have lesser time stamp than null message’s time stamp are safe to process. It means that there won’t be any arrival of any events from that logical process until current simulation time is equal to the time stamp of the null message. With the time stamp of the null message, a Lookahead value is added to the time stamp of that null message. This Lookahead value can be measure on certain kind of parameters such as delay to transmit a message, propagation delay, etc. therefore, calculating value of Lookahead is the most important part as Lookahead value affects the performance of the conservative distributed event simulation. Proper value of Lookahead can reduce the number of null messages which decreases the traffic of the network. In this paper, we demonstrate some calculation on the Lookahead which shows the performance of the distributed event simulation

A Simulation Model for Hierarchical Routing Protocols in Wireless Sensor Networks

One of the critical issues in wireless sensor network is power saving scheme as network should be considered to operate more efficiently. The sensor nodes are usually operated by a finite number of batteries and it should have a certain lifetime for gathering, processing, and transmitting information. Since some sensor nodes may fail due to lack of power, this consideration has led to give more interest about routing protocols. Depending on the network structure, a sensor network can be hierarchical or cluster-based hierarchical model, where the nodes will play different roles in the networks. We present three different types of routing protocols: LEACH, PEGASIS, and VGA, several simulations are conducted to analyze the performance of these protocols including the power consumption and overall network performance. On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased

Formalization of the prediction and ranking of software development life cycle models

The study of software engineering professional practices includes the use of the formal methodology in a software development. Identifying the appropriate methodology will not only reduce the failure of software but will also help to deliver the software in accordance with the predetermined budget and schedule. In literature, few works have been developed a tool for prediction of the most appropriate methodology for the specific software project. In this paper, a method for selecting an appropriate software development life cycle (SDLC) model based on a ranking manner from the highest to the lowest scoring is presented. The selection and ranking of appropriate SDLC elaborate the related SDLC’s critical factors, these factors are given different weights according to the SDLC, then these weights are used by the proposed mathematical method. The proposed approach has been extensively experimented on a dataset by software practitioners who are working in the software industry. Experimental results show that, the proposed method represents an applicable tool in predicting and ranking suitable SDLC models on various types of projects, such as: life-critical systems, commercial uses systems, and entertainment applications