Technology Development Standardization and Evaluation in Pulmonary Medicine

book chapterBiomedical Informatic

Video-based patient monitoring system application of the system in intensive care unit

The paper presents the video-based monitoring system to assess the physiological parameters and patient state in intensive care unit. It allows to measure thoracic and abdominal breathing movements, remote plethysmography signals, tissue perfusion, patient activity and changes in psycho-emotional state. Thus, the system provides a comprehensive assessment of patient state without contact. The system works in usual illumination conditions of intensive care unit and consists of a personal computer with specialized software and two low-cost Logitech C920 webcams with RGB sensors (8 bit per channel), 30 Hz sampling frequency and 640x480 pixel resolution. The webcams were placed at a distance of 80 cm above the patient’s body. The software provides automatic assessment of psychophysiological parameters and determination the following patterns: heart rate, heart rate variability, asystole and arrhythmias, breathing rate, spontaneous breathing recovery, breathing muscle tone and patient consciousness recovery, motor activity and control of ventilation parameters. The proposed system can be used as an additional diagnostic tool of anesthesia equipment for non-invasive patient monitoring in intensive care unit. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reservedThe work was partially supported by Act 211 Government of the Russian Federation, contract 02.A 03.21.0006

Research and technology

Significant research and technology activities at the Johnson Space Center (JSC) during Fiscal Year 1990 are reviewed. Research in human factors engineering, the Space Shuttle, the Space Station Freedom, space exploration and related topics are covered

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Intensive-care unit patients monitoring by computer vision system

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2013, Director: Santi Seguí MesquidaIn this project, we propose an automatic computer vision system for patient monitoring at the Intensive-Care Unit (ICU). These patients require constant monitoring and, due to the high costs associated to equipment and staff necessary, the design of an automatic system would be helpful. Depth imaging technology has advanced dramatically over the last few years, finally reaching a consumer price point with the launch of Kinect. These depth images are not affected by the lighting conditions and provide us a good vision, even without any light, so we can monitorize the patients 24 hours a day. In this project, we worked on two of the parts of the object detection systems: the descriptor and classifier. Concerning the descriptor, we analyzed the performance of one of the most used descriptors for object detection in RGB images, the Histogram of Oriented Gradients, and we have proposed a descriptor designed for depth images. It is shown that the combination of these two descriptors increases system accuracy. As to the detection, we have done various tests. We analyzed the detection of patient body parts separately, and we have used a model where the patient is divided into multiple parts and each part is modeled with a set of templates, demonstrating that the use of a model helps to improve detection

Adaptive Manufacturing for Healthcare During the COVID-19 Emergency and Beyond

During the COVID-19 pandemic, global health services have faced unprecedented demands. Many key workers in health and social care have experienced crippling shortages of personal protective equipment, and clinical engineers in hospitals have been severely stretched due to insufficient supplies of medical devices and equipment. Many engineers who normally work in other sectors have been redeployed to address the crisis, and they have rapidly improvised solutions to some of the challenges that emerged, using a combination of low-tech and cutting-edge methods. Much publicity has been given to efforts to design new ventilator systems and the production of 3D-printed face shields, but many other devices and systems have been developed or explored. This paper presents a description of efforts to reverse engineer or redesign critical parts, specifically a manifold for an anaesthesia station, a leak port, plasticware for COVID-19 testing, and a syringe pump lock box. The insights obtained from these projects were used to develop a product lifecycle management system based on Aras Innovator, which could with further work be deployed to facilitate future rapid response manufacturing of bespoke hardware for healthcare. The lessons learned could inform plans to exploit distributed manufacturing to secure back-up supply chains for future emergency situations. If applied generally, the concept of distributed manufacturing could give rise to “21st century cottage industries” or “nanofactories,” where high-tech goods are produced locally in small batches

Pre-conference proceedings of the 3rd IFIP TC 13.6 HWID working conference

The committees under IFIP include the Technical Committee TC13 on Human – Computer Interaction within which the work of this volume has been conducted. TC 13 on Human-Computer Interaction has as its aim to encourage theoretical and empirical human science research to promote the design and evaluation of human-oriented ICT. Within TC 13 there are different Working Groups concerned with different aspects of Human-Computer Interaction. The flagship event of TC13 is the bi-annual international conference called INTERACT at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high. Publications arising from these TC13 events are published as conference proceedings such as the INTERACT proceedings or as collections of selected and edited papers from working conferences and workshops. See http://www.ifip.org/ for aims and scopes of TC13 and its associated Working Group

Spectroscopy detects skeletal muscle microvascular dysfunction during onset of sepsis in a rat fecal peritonitis model

Sepsis is a dysregulated host inflammatory response to infection potentially leading to life-threatening organ dysfunction. The objectives of this study were to determine whether early microvascular dysfunction (MVD) in skeletal muscle can be detected as dynamic changes in microvascular hemoglobin (MVHb) levels using spectroscopy and whether MVD precedes organ histopathology in septic peritonitis. Skeletal muscle of male Sprague–Dawley rats was prepared for intravital microscopy. After intraperitoneal injection of fecal slurry or saline, microscopy and spectroscopy recordings were taken for 6 h. Capillary red blood cell (RBC) dynamics and SO2 were quantified from digitized microscopy frames and MVHb levels were derived from spectroscopy data. Capillary RBC dynamics were significantly decreased by 4 h after peritoneal infection and preceded macrohemodynamic changes. At the same time, low-frequency oscillations in MVHb levels exhibited a significant increase in Power in parts of the muscle and resembled oscillations in RBC dynamics and SO2. After completion of microscopy, tissues were collected. Histopathological alterations were not observed in livers, kidneys, brains, or muscles 6 h after induction of peritonitis. The findings of this study show that, in our rat model of sepsis, MVD occurs before detectable organ histopathology and includes ~ 30-s oscillations in MVHb. Our work highlights MVHb oscillations as one of the indicators of MVD onset and provides a foundation for the use of non-invasive spectroscopy to continuously monitor MVD in septic patients

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective