An automatic sleep apnea analysis with soft computing approaches

University of Technology, Sydney. Faculty of Engineering and Information Technology.Sleep Apnea (SA) is a common disorder without “age-specific” that affects approximately 2% of women and 4% of men; sleep apnea is characterized by repetitive cessation of breathing during sleep. The consequences of the sleep apnea include daytime sleepiness, impaired cognitive function, impaired memory, neurocognitive dysfunction, and development of cardiovascular disorders, metabolic dysfunction, and impaired quality of life. This thesis investigates the automated detection and prediction of sleep apnea. Many researchers have concentrated on automated detection of sleep apnea, but not much comprehensive or well-ordered work has been done on signal and feature selection or on predicting of the sleep apnea. The objective is to find the best set of signals as input and the best set of features from selected signals that can be used by a machine learning approaches to study sleep apnea. The best set here is not only refers to a smallest set of signals with a good performance in sleep apnea analysis but also consideration for a set of signals that can be easily acquired from patients. During the course of this thesis, several algorithms were developed. These algorithms can be used in sleep apnea studies or in wider machine learning areas. The most important contributions of this thesis can be summarized as below: -Developing a new signal segmentation algorithm designed specifically for sleep apnea by attention to its properties. This algorithm chose times windows with a greater probability of containing at least one sleep apnea event. After that these segmentations are generated, they should be reviewed by the machine learning approaches to be classified as sleep apnea or normal. -Developing a novel Support Vector Machine (SVM)-based approach named Self-Advising Support Vector Machine (SA-SVM) that transfers more knowledge from the training phase of SVM to the test phase. This idea helps SVM to learn from misclassified data in training phase and use this gained knowledge, in the testing phase. This approach can be used in any binary classification problems and it shows also high impact in sleep apnea detection. -Developing a new parallel structure for Particle Swarm Optimisation (PSO). Finding the best set of input signals or the best set of features required a huge amount of computation power which a single PSO – or other optimisation approaches- cannot deal with, so a new hierarchical multi-master structure for parallel PSO was developed in this thesis, which quickly revealed its advantages over previous parallel PSO structures. In this thesis real data has been used from Concord Repatriation General Hospital in Sydney. Obtained result shows a good performance in detection and classification of sleep apnea. Together with detection and classification, a prediction of sleep apnea was also considered. The prediction stage examines some famous neural networks structures and demonstrated how to improve the final result by taking advantage of multi neural network approach

Index finger motion recognition using self-advise support vector machine

Because of the functionality of an index finger, the disability of its motion in the modern age can decrease the person's quality of life. As a part of rehabilitation therapy, the recognition of the index finger motion for rehabilitation purposes should be done properly. This paper proposes a novel recognition system of the index finger motion suing a cutting-edge method and its improvements. The proposed system consists of combination of feature extraction method, a dimensionality reduction and well-known classifier, Support Vector Machine (SVM). An improvement of SVM, Self-advise SVM (SA-SVM), is tested to evaluate and compare its performance with the original one. The experimental result shows that SA-SVM improves the classification performance by on average 0.63 %

Self-advising SVM for sleep apnea classification

In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to trans-fer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, ob-structive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification

A geometrical sink-based cooperative coverage hole recovery strategy for WSNs

© 2015 IEEE. Unlike sporadic node failures, coverage holes emerging from multiple temporally-correlated node failures can severely affect quality of service in a network and put the integrity of entire wireless sensor networks at risk. Conventional topology control schemes addressing such undesirable topological changes have usually overlooked the status of participating nodes in the recovery process with respect to the deployed sink node(s) in the network. In this paper, a cooperative coverage hole recovery model is proposed which utilises the simple geometrical procedure of circle inversion. In this model, autonomous nodes consider their distances to the deployed sink node(s) in addition to their local status, while relocating towards the coverage holes. By defining suitable metrics, the performance of our proposed model performance is compared with a force-based approach

Hydrodynamic drag-force measurement and slip length on microstructured surfaces

International audienceIn this paper, a drainage experiment of water between a borosilicate sphere and a microstructured surface constituted by regularly spaced pillars is presented. The microstructured surface has two parts: on one part the liquid forms a Cassie interface and on the second it forms a Wenzel interface. The measured hydrodynamic drag force is larger on the Cassie part compared to the Wenzel part. Furthermore, for the Cassie part, from the hydrodynamic drag force measurements on a pillar and between pillars the corresponding local slip lengths have been extracted. The area average slip length on the surface is in agreement with the value expected by Philip's equation

Graphene Transistor as a Probe for Streaming Potential

We explore the dependence of electrical transport in a graphene field effect transistor (GraFET) on the flow of the liquid within the immediate vicinity of that transistor. We find large and reproducible shifts in the charge neutrality point of GraFETs that are dependent on the fluid velocity and the ionic concentration. We show that these shifts are consistent with the variation of the local electrochemical potential of the liquid next to graphene that are caused by the fluid flow (streaming potential). Furthermore, we utilize the sensitivity of electrical transport in GraFETs to the parameters of the fluid flow to demonstrate graphene-based mass flow and ionic concentration sensing. We successfully detect a flow as small as~70nL/min, and detect a change in the ionic concentration as small as ~40nM.Comment: 6 pages, 4 figure

Hydrodynamic slip can align thin nanoplatelets in shear flow

The large-scale processing of nanomaterials such as graphene and MoS2 relies on understanding the flow behaviour of nanometrically-thin platelets suspended in liquids. Here we show, by combining non-equilibrium molecular dynamics and continuum simulations, that rigid nanoplatelets can attain a stable orientation for sufficiently strong flows. Such a stable orientation is in contradiction with the rotational motion predicted by classical colloidal hydrodynamics. This surprising effect is due to hydrodynamic slip at the liquid-solid interface and occurs when the slip length is larger than the platelet thickness; a slip length of a few nanometers may be sufficient to observe alignment. The predictions we developed by examining pure and surface-modified graphene is applicable to different solvent/2D material combinations. The emergence of a fixed orientation in a direction nearly parallel to the flow implies a slip-dependent change in several macroscopic transport properties, with potential impact on applications ranging from functional inks to nanocomposites.Energy Technolog

Global Impact of the COVID-19 Pandemic on Cerebral Venous Thrombosis and Mortality

Background and purpose: Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year. Methods: We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020). Results: There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P<0.0001) and 2020 (41.4%; 95% CI, 37.0 to 46.0; P<0.0001). A COVID-19 diagnosis was present in 7.6% (132/1,738) of CVT hospitalizations. CVT was present in 0.04% (103/292,080) of COVID-19 hospitalizations. During the first pandemic year, CVT mortality was higher in patients who were COVID positive compared to COVID negative patients (8/53 [15.0%] vs. 41/910 [4.5%], P=0.004). There was an increase in CVT mortality during the first 5 months of pandemic years 2020 and 2021 compared to the first 5 months of the pre-pandemic year 2019 (2019 vs. 2020: 2.26% vs. 4.74%, P=0.05; 2019 vs. 2021: 2.26% vs. 4.99%, P=0.03). In the first 5 months of 2021, there were 26 cases of vaccine-induced immune thrombotic thrombocytopenia (VITT), resulting in six deaths. Conclusions: During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT