A Time-Series Approach to Predict Obstructive Sleep Apnea (OSA) Episodes

Abstract -Sleep apnea is a common respiratory disorder during sleep. It is characterized by pauses in breathing or shallow breathing during sleep for longer than 10 seconds. Except the fact that not having a proper sleep and being rested for the next day, in some cases the apnea period (not breathing interval) may last more than 30 seconds and this situation can even be fatal. 14% of men and 5% of women suffer from Obstructive Sleep Apnea (OSA) in United States. Patients may experience apnea for more than 300 times in a single night sleep. Polysomnography (PSG) is a multi-parametric recording of biophysiological changes, containing EEG, ECG, SpO2, Nasal Airflow signals, performed during overnight sleep. In this study, a fully automatic apnea detection algorithm is developed and an early warning system is proposed to predict OSA episodes by extracting time-series features of OSA periods and regular respiration using nasal airflow signal. Extracted features are then reduced to improve the performance of the prediction. Support vector machines (SVM), one of the commonly used classification algorithms in medical applications, is implemented for learning and prediction of the OSA episodes. The results show that OSA episodes are predicted with 87.6% of accuracy and 91.3% of sensitivity, 30 seconds before patient faces apnea. By this approach, apnea related health risks can be minimized by foreknowledge

Plasma‐Assisted Surface Modification and Heparin Immobilization: Dual‐Functionalized Blood‐Contacting Biomaterials with Improved Hemocompatibility and Antibacterial Features

The inferior hemocompatibility or antibacterial properties of blood-contacting materials and devices are restraining factors that hinder their successful clinical utilization. To highlight these, a plasma-enhanced modification strategy is favored for surface tailoring of an extensively used biomaterial, polypropylene (PP). The surface activation of the PPs is achieved by oxygen plasma etching and subsequent surface functionalization through amine-rich precursor mediated coating by plasma glow discharge. After optimum plasma processing parameters are decided, heparin (anticoagulant and antithrombic drug) is either attached or covalently conjugated on the PPs’ surfaces. The aminated films produced at 75 W plasma power with 15 min exposure time are highly hydrophilic (34.72 ± 5.92°) and surface active (65.91 mJ m$^{-2}$), facilitating high capacity heparin immobilization (≈440 µg cm$^{-2}$) by covalent linkage. The kinetic-blood coagulation rate and protein adhesion amount on the plasma-mediated heparinized PPs are decreased about tenfold and 15-fold, and platelet adhesion is markedly lowered. In addition, heparinized-PP surfaces comprise superior antibacterial activity against gram-positive/-negative bacteria conveyed particularly by contact-killing (99%). The heparin-coating did not cause cytotoxicity on fibroblast cells, instead enhanced their proliferation, as shown by the (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay. Overall, this simple methodology is highly proficient in becoming a universal strategy for developing dual-functionalized blood-contacting materials

A Medical Centrifuge System Based on the Switched Reluctance Motor

A prototype medical centrifuge system based on switched reluctance motors (SRMs) is designed and realized in this study. Although medical centrifuge systems use asynchronous motors or motor structures with permanent magnets in general, an SRM had many advantages compared to other types of electrical motors is designed and used as a drive motor in the study. A driver unit to operate the system under desired conditions and all mechanical components are designed and manufactured in the study. Therefore, the study shows that the cost effective medical centrifuge systems can be produced with different technological specifications

Special Issue: Autonomous Low Power Monitoring Sensors

Geo-distributed autonomous low-power sensing nodes – e.g., wireless sensor networks (WSN) and Internet of Things (IoT) networks - are harbouring more autonomous capabilities in the concepts of Internet of Everything (IoE) and Automation of Everything (AoE) [1] to serve more intelligent multiple larger synergistic cyber-physical systems (CPS) that perform more complex autonomous tasks by removing the human out of the loop [2]. They can be useful for scientific works, such as to build reliable climate models, by providing continuous series of some in-situ environment variables over years at high frequency (e.g. soil moisture in a catchment at 1 point/10 min). Due to spatial heterogeneity variables have to be retrieved at different points, which requires a network of such sensors over large area (> 1 km2 every 100 m) However, the capabilities of sensor nodes within these domains and others are still highly limited concerning energy, storage, processing, sensing coverage, measurement quality, robustness, communication with risk of loss of data (e.g., network connectivity, low data rate in low-powered wide-area network (LPWAN)), latency, costs and cybersecurity [3]. These limitations, or contradictory requirements, need to be mitigated, partly or not depending on the sensor purpose. Moreover, energy-efficient deployment and data routing of low-power sensor nodes to lessen their energy consumption is of prime importance for prolonging their own network lifetime. With these in mind, this Special Issue covers topics related to autonomous resource-constraint sensor nodes, particularly, focuses on research attempts to improve their capabilities with increasing autonomous abilities leading to efficient adaption to their environment and meeting the different requirements. We would like to invite the academic and industrial research community to submit original research as well as review articles to this Special Issue. Topics of interest include: Main Topics: - Low cost sensor - Quality measurement - Rugged autonomous sensor - Autonomous resource-constraint sensor nodes - Design and development of autonomous low-power sensors - Energy-efficient deployment and data routing of low-power sensor nodes - WSN communication topology - Intelligent energy harvesting (EH) in autonomous low power monitoring sensors - Self-prognosis, self-diagnosis and self-healing autonomous monitoring sensors - Sensor networks - Lifespan of a sensor network - Multi low-power sensor integration - End-to-end wireless communication of autonomous monitoring sensors - Mobile data collection sinks in sensor networks - Cybersecurity in sensors - Miniaturized sensors - Autonomous health monitoring (i.e., fault diagnosis) of remotely performing critical devices (e.g., power outage avoidance systems, condition monitoring of wind turbines or railways) - Medical and biomedical sensing with low-power sensors - Implementation of wireless sensors in urban environment, industry, agriculture, remote observatory - Implementation of low-power sensors in the cloud and edge computing - Indoor implementation of wireless sensors (e.g, security, fire monitoring, energy efficiency of buildings and structures) - Disaster management using low-power sensors (flood, earthquake, tsunami) - Computational intelligence in sensors - Big data management in geo-distributed sensors - Intelligent vision-based low-power sensors 1. K. Kuru and H. Yetgin, "Transformation to Advanced Mechatronics Systems Within New Industrial Revolution: A Novel Framework in Automation of Everything (AoE)," in IEEE Access, vol. 7, pp. 41395-41415, 2019. https://doi.org/10.1109/ACCESS.2019.2907809 2. K. Kuru, “Management of geo-distributed intelligence: Deep Insight as a Service (DINSaaS) on Forged Cloud Platforms (FCP),” in Journal of Parallel and Distributed Computing, 149 . pp. 103-118, 2021. https://doi.org/10.1016/j.jpdc.2020.11.009 3. K. Kuru, "Planning the Future of Smart Cities with Swarms of Fully Autonomous Unmanned Aerial Vehicles using a Novel Framework," in IEEE Access, vol. 9, pp. 6571-6595, 2021. https://doi.org/10.1109/ACCESS.2020.304909

Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis

In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively

Down Syndrome Diagnosis Based on Gabor Wavelet Transform

Down syndrome is a chromosomal condition caused by the presence of all or part of an extra 21st chromosome. It has different facial symptoms. These symptoms contain distinctive information for face recognition. In this study, a novel method is developed to distinguish Down Syndrome in a custom face database. Gabor Wavelet Transform (GWT) is used as a feature extraction method. Dimension reduction is performed with Principal Component Analysis (PCA). New dimension which has most valuable information is derived with Linear Discriminant Analysis (LDA). Classification process is implemented with k-nearest neighbor (kNN) and Support Vector Machine (SVM) methods. The classification accuracy is carried out 96% and 97,34% with kNN and SVM methods, respectively. Different from the studies related with the Down Sydrome, feature selection process is applied before PCA according to the correlation between components of feature vectors. Best results are achieved with euclidean distance metric for kNN and linear kernel type for SVM. In this way, we developed an efficient system to recognize Down syndrome

Differentiation of Benign and Malignant Thyroid Nodules with ANFIS by Using Genetic Algorithm and Proposing a Novel CAD-Based Risk Stratification System of Thyroid Nodules.

The thyroid nodule risk stratification guidelines used in the literature are based on certain well-known sonographic features of nodules and are still subjective since the application of these characteristics strictly depends on the reading physician. These guidelines classify nodules according to the sub-features of limited sonographic signs. This study aims to overcome these limitations by examining the relationships of a wide range of ultrasound (US) signs in the differential diagnosis of nodules by using artificial intelligence methods. An innovative method based on training Adaptive-Network Based Fuzzy Inference Systems (ANFIS) by using Genetic Algorithm (GA) is used to differentiate malignant from benign thyroid nodules. The comparison of the results from the proposed method to the results from the commonly used derivative-based algorithms and Deep Neural Network (DNN) methods yielded that the proposed method is more successful in differentiating malignant from benign thyroid nodules. Furthermore, a novel computer aided diagnosis (CAD) based risk stratification system for the thyroid nodule’s US classification that is not present in the literature is proposed

A hipericina aumenta a eficácia do laser de alta potência? Um estudo preliminar e experimental em ratos

CONTEXT AND OBJECTIVE: Lasers are widely used in treating symptomatic benign prostatic hyperplasia. In current practice, potassium titanyl phosphate (KTP) lasers are the most common type of laser systems used. The aim here was to evaluate the rapid effect of high-power laser systems after application of hypericin. DESIGN AND SETTING: Experimental animal study conducted in the Department of Urology, Gülhane Military Medical Academy, Ankara, Turkey, in 2012. METHODS: Sixteen rats were randomized into four groups: 120 W KTP laser + hypericin; 120 W KTP laser alone; 80 W KTP laser + hypericin; and 80 W KTP laser alone. Hypericin was given intraperitoneally two hours prior to laser applications. The laser incisions were made through the quadriceps muscle of the rats. The depth and the width of the laser incisions were evaluated histologically and recorded. RESULTS: To standardize the effects of the laser, we used the ratio of depth to width. These new values showed us the depth of the laser application per unit width. The new values acquired were evaluated statistically. Mean depth/width values were 231.6, 173.6, 214.1 and 178.9 in groups 1, 2, 3 and 4, respectively. The most notable result was that higher degrees of tissue penetration were achieved in the groups with hypericin (P < 0.05). CONCLUSIONS: The encouraging results from our preliminary study demonstrated that hypericin may improve the effects of KTP laser applications.CONTEXTO E OBJETIVO: Lasers são amplamente utilizados no tratamento de hiperplasia benigna de próstata sintomática. Na prática atual, lasers de fosfato de titanilo de potássio (KTP) são os tipos mais comuns usados dos sistemas. O objetivo foi avaliar o efeito rápido do sistema laser de alta potência após a aplicação de hipericina. TIPO DE ESTUDO E LOCAL: Estudo experimental animal, realizado no Departamento de Urologia, Academia de Medicina Militar de Gülhane, Ancara, Turquia, em 2012. MÉTODOS: 16 ratos foram divididos aleatoriamente em 4 grupos: 120W KTP laser + hipericina; 120W KTP laser somente; 80W KTP laser + hipericina; 80W KTP laser somente. Hipericina foi dada intraperitonealmente duas horas antes da aplicação do laser. As incisões a laser foram feitas através do músculo quadríceps dos ratos. A profundidade e a largura das incisões a laser foram avaliadas histologicamente e registradas. RESULTADOS: Para padronizar o efeito do laser foi utilizada a razão entre profundidade e largura. Estes novos valores nos mostraram a profundidade da aplicação do laser de largura por unidade. Os novos valores adquiridos foram avaliados estatisticamente. Os valores da média de profundidade/largura foram 231,6, 173,6, 214,1 e 178,9 nos grupos 1, 2, 3 e 4, respectivamente. O resultado mais notável foi atingir altos graus de penetração tecidual nos grupos com hipericina (P < 0,05). CONCLUSÕES: Os resultados promissores do nosso estudo preliminar mostraram que hipericina pode melhorar os efeitos das aplicações do laser KTP