Special Issue "Patient Triage & Telemedicine Post COVID19: Sensors and Solutions for Monitoring and Management in Hospital and at Home"
Deadline for manuscript submissions: 31 December 2020.
Interests: Wearable and electrode-less physiological monitoring, Brain-computer interface, Biomedical engineering, clinical engineering
Special Issues and Collections in MDPI journals
Interests: Blind source separation; Independent Component Analysis; Biomedical Signal Processing; Human Computer Interaction; Pattern Recognition
Special Issues and Collections in MDPI journals
Interests: biomedical engineering; neuromorphic engineering; mixed-signal integrated circuit design; medical devices; machine learning; circuits and systems for implantable and wearable biomedical devices
Special Issues and Collections in MDPI journals
Patient triage has always played a key role in emergency treatment and hospital admission. Providing accurate and timely assessments of seriously ill patients, based on urgency, is what makes the triage system work. However, triage nurses need to make decisions and initiate the correct patient journey among hundreds of possible presentation scenarios in the shortest timeframe possible. The recent COVID19 pandemic has dramatically increased the importance of correct triage and radically changed the concept of triage. In first instance the modality to approach patients has changed i.e. PPE requirements and continues monitoring of hospital/triage personnel. Secondly the necessity of triaging patients outside of the hospital and avoid presentation of COVID19 patients in a crowded Emergency Department is now paramount. In third instance, tele-triaged patients receiving therapy at home have dramatically increased the demand on the telemedicine systems.
In the last couple of decades, wearable and personal technologies have opened new scenarios for patient/personal health monitoring and patients are now carrying their devices upon hospital admission allowing for objective and quantitative assessment of physical conditions inclusive of past data. These devices are so widespread that also nurses/doctors might use one during their workhours. Some of these devices are now integrated (or integration is underway) into telemedicine solutions.
This Special Issue will explore new solutions in this vast emerging scenario, contributions that address (but not restricted to) the following topics are welcome:
- Case studies addressing the use of already available solutions repurposed to address the novel needs
- Triage aided by wearable sensors
- Integration of pervasive ubiquitous monitoring system into traditional triage systems and decision making
- Management of triage
- Triage suitable sensors
- Precision Triage
- Triage during pandemic outbreak
- Triage of triage personnel
Dr. Tara Hamilton
Dr. Ganesh R. Naik
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Wearable systems
- Integration of multiple sensors information
- Continuous sensing data synthesis
- Patient monitoring
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Novel, Contactless, Portable “Spot-Check” Device Accurately Measures Respiratory Rate
Authors: William Daw,1 Ruth Kingshott,1 Reza Saatchi,3 Derek Burke2, Robert Evans, Alan Holloway, Jon Travis, Anthony Jones4, Ben Hughes, Heather E Elphick,1,3
Affiliation: 1 Respiratory Unit, Sheffield Children’s Hospital, Sheffield, UK 2 Emergency Department, Sheffield Children’s Hospital, Sheffield UK 3 Faculty of ACES, Sheffield Hallam University, Sheffield, UK 4Design Futures, Faculty of STA, Sheffield Hallam University, Sheffield, UK
Abstract: Background: Respiratory rate (RR) is an important vital sign used in the initial and ongoing assessment of acutely ill patients. It is also used as a predictor of serious deterioration in a patient's clinical condition. Convenient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although devices which measure RR exist, none has entered everyday clinical practice. Objectives: We have developed a contactless portable respiratory rate monitor (CPRM) and evaluated the agreement in respiratory rate measurements between existing methods and our new device. The CPRM uses thermal anemometry to measure breath signals during inspiration and expiration. Method: RR data were collected from 52 healthy adult volunteers using respiratory inductance plethysmography (RIP) bands (established contact method), visual counting of chest movements (established non-contact method) and the CPRM (new method), simultaneously. Two differently shaped funnel attachments to the CPRM were evaluated for each volunteer. Results: Data showed good agreement between measurements from the CPRM and the gold standard RIP, with intra-class correlation coefficient (ICC): 0.836, mean difference 0.46 and 95% limits of agreement of -5.90 to 6.83. When separate air inlet funnels of the CPRM were analysed, stronger agreement was seen with an elliptical air inlet; ICC 0.908, mean difference 0.37 with 95% limits of agreement -4.35 to 5.08. Conclusions: A contactless device for accurately and quickly measuring respiratory rate will be an important triage tool in the clinical assessment of patients. More testing is needed to explore the reasons for outlying measurements and to evaluate in the clinical setting.
Title: Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall
Authors: Emilio Andreozzi; Antonio Fratini; Daniele Esposito; Ganesh Naik; Caitlin Polley; Gaetano D. Gargiulo; Paolo Bifulco
Affiliation: Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, University “Federico II” of Naples, Italy
Abstract: This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the XIX century, several techniques have been proposed to detect the mechanical vibrations caused by the cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being the seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject’s chest and provides information on major events of cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto subject’s chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.
Title: Small silicone encased piezoelectric sensor for wearable Bluetooth triage healthcare monitoring.
Authors: Caitlin Polley; Titus Jayarathna; Upul Gunawardana; Ganseh Naik; Tara Hamilton; Emilio Andreozzi; Paolo Bifulco; Daniele Esposito; Gaetano Gargiulo
Affiliation: School of Engineering, Western Sydney University
Abstract: Triage assessment is the first interaction between a patient presenting to an emergency department and a nurse/paramedic. This assessment is a highly dynamic process that requires initial rapid assessment followed by routine check on the patients vitals: including respiratory rate, temperature and pulse rate. Ideally these checks should be performed continuously and remotely to reduce workload on triage nurses. While there are international grading systems, optimising tools and monitoring systems can be introduced with a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. For this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome to enhance contact with skin to detect wirelessly both respiration and cardiac rates at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. Data shows that the device has an extremely high sensitivity for cardiac events with an averaged (across all tested positions) positive predicted value of 99.73% as well as an averaged positive predictive rate of 96.78% for respiration detection.
Title: Heartbeat Classification Using Nonlinear Morphological Features and Voting Method: Inter-Patient Scheme
Authors: Rajesh N V P S Kandala; Ravindra Dhuli; Paweł Pławiak; Ganesh R Naik; Hossein Moeinzadeh; Gaetano D. Gargiulo; Suryanarayana Gunnam
Affiliation: Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warsaw 24 st., F-3, 31-155 Krakow, Poland
Abstract: Heart diseases because of abnormal heart rhythms are one of the significant concerns of global mortality. Manual recognition of these irregular beats is not only a tedious task but is error-prone. This work presents an automated heartbeat classification of imbalanced data groups - for heart disease identification, which is crucial in enhancing diagnostic quality. In this work, we propose a new method based on nonlinear morphological features and a majority voting scheme. For feature extraction, a non-stationary and nonlinear decomposition method is used-namely, improved complete ensemble empirical mode decomposition to retrieve the implicit information in the electrocardiogram. Since it is a data-driven approach, the decomposed modes can capture the morphology of the signal. Various entropy and higher-order statistic measures are then calculated from these modes to serve as features. The features are then fed to a voting model. The simulation results show the superiority of the proposed method especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100% sensitivity and are appreciable when compared with state-of-the-art results. Our proposed method demonstrates significant performance with more than 70% average sensitivity of overall classification in case of AAMI inter-patient heartbeat classification scenario. In this scenario, we use mutually anonymous train and test datasets. This enables implementation of real-time computer-aided diagnosis model. Moreover, class imbalance is one of the critical challenges in medical diagnoses. The proposed scheme also helps to resolve class imbalance via an algorithmic level approach.