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Elvekjaer M, Carlsson CJ, Rasmussen SM, Porsbjerg CM, Grønbæk KK, Haahr-Raunkjær C, Sørensen HBD, Aasvang EK, Meyhoff CS. Agreement between wireless and standard measurements of vital signs in acute exacerbation of chronic obstructive pulmonary disease: a clinical validation study. Physiol Meas 2021; 42. [PMID: 33984846 DOI: 10.1088/1361-6579/ac010c] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 05/13/2021] [Indexed: 11/11/2022]
Abstract
Objective.Wireless sensors for continuous monitoring of vital signs have potential to improve patient care by earlier detection of deterioration in general ward patients. We aimed to assess agreement between wireless and standard (wired) monitoring devices in patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).Approach.Paired measurements of vital signs were recorded with 15 min intervals for two hours. The primary outcome was agreement between wireless and standard monitor measurements using the Bland and Altman method to calculate bias with 95% limits of agreement (LoA). We considered LoA of less than ±5 beats min-1(bpm) acceptable for heart rate (HR), whereas agreement of peripheral oxygen saturation (SpO2), respiratory rate (RR), and blood pressure (BP) were acceptable if within ±3%-points, ±3 breaths min-1(brpm), and ±10 mmHg, respectively.Main results.180 sample-pairs of vital signs from 20 with AECOPD patients were recorded for comparison. The wireless versus standard monitor bias was 0.03 (LoA -3.2 to 3.3) bpm for HR measurements, 1.4% (LoA -0.7% to 3.6%) for SpO2, -7.8 (LoA -22.3 to 6.8) mmHg for systolic BP and -6.2 (LoA -16.8 to 4.5) mmHg for diastolic BP. The wireless versus standard monitor bias for RR measurements was 0.75 (LoA -6.1 to 7.6) brpm.Significance.Commercially available wireless monitors could accurately measure HR in patients admitted with AECOPD compared to standard wired monitoring. Agreement for SpO2were borderline acceptable while agreement for RR and BP should be interpreted with caution.
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Affiliation(s)
- Mikkel Elvekjaer
- Department of Anaesthesia and Intensive Care, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.,Department of Anaesthesiology, Centre for Cancer and Organ Diseases, Rigshospitalet, University of Copenhagen, Denmark
| | - Christian Jakob Carlsson
- Department of Anaesthesia and Intensive Care, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.,Department of Anaesthesiology, Centre for Cancer and Organ Diseases, Rigshospitalet, University of Copenhagen, Denmark
| | - Søren Møller Rasmussen
- Biomedical Engineering, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Celeste M Porsbjerg
- Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.,Respiratory Research Unit, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Katja Kjær Grønbæk
- Department of Anaesthesia and Intensive Care, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.,Department of Anaesthesiology, Centre for Cancer and Organ Diseases, Rigshospitalet, University of Copenhagen, Denmark
| | - Camilla Haahr-Raunkjær
- Department of Anaesthesia and Intensive Care, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.,Department of Anaesthesiology, Centre for Cancer and Organ Diseases, Rigshospitalet, University of Copenhagen, Denmark
| | - Helge B D Sørensen
- Biomedical Engineering, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Eske K Aasvang
- Department of Anaesthesiology, Centre for Cancer and Organ Diseases, Rigshospitalet, University of Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Christian S Meyhoff
- Department of Anaesthesia and Intensive Care, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.,Department of Clinical Medicine, University of Copenhagen, Denmark
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Slapničar G, Wang W, Luštrek M. Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach. SENSORS (BASEL, SWITZERLAND) 2021; 21:1836. [PMID: 33800716 PMCID: PMC7961385 DOI: 10.3390/s21051836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 11/16/2022]
Abstract
Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.
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Affiliation(s)
- Gašper Slapničar
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Wenjin Wang
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; or
- Philips Research Eindhoven, 5656 AE Eindhoven, The Netherlands
| | - Mitja Luštrek
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
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Shi W, Sun Y, Li S, Cao Q, Wang B. Spatial and Temporal Feature-Based Reduced Reference Quality Assessment for Rate-Varying Videos in Wireless Networks. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001419500216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
For the impact of the bitrate change of video streaming services according to the available bandwidth on user satisfaction, in this paper, we propose a spatial and temporal feature-based reduced reference (RR) quality assessment for rate-varying videos in wireless networks called STRQAW. First, simulating the orientation selectivity mechanism of the human visual system (HVS), the histogram of the orientation selectivity-based visual pattern in each frame is extracted as the spatial feature. The histogram similarity between the rate-varying video and the original video is computed as the spatial metric. Second, we extract the temporal variation of the DCT coefficients of the consecutive frame differences as the temporal feature. The temporal variation similarity between the rate-varying video and the original video is calculated as the temporal metric. Finally, we take into account the recency effect and assess the overall quality by combining the temporal and spatial metric. The experimental results using the Laboratory for Image and Video Engineering (LIVE) mobile video quality assessment (VQA) database show that STRQAW is consistent with the subjective assessment results, which means it reflects human subjective feelings well and it provides an evaluation for adjusting compression-coding rates in real time. STRQAW can be used to guide video application providers and network operators working towards satisfying end-user experiences.
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Affiliation(s)
- Wenjuan Shi
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, P. R. China
- School of New Energy and Electronical Engineering, Yancheng Teachers University, Yancheng, P. R. China
| | - Yanjing Sun
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, P. R. China
| | - Song Li
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, P. R. China
| | - Qi Cao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, P. R. China
| | - Bowen Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, P. R. China
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Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR. Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1802. [PMID: 29865289 PMCID: PMC6022164 DOI: 10.3390/s18061802] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 05/27/2018] [Accepted: 05/27/2018] [Indexed: 11/24/2022]
Abstract
Traditional methods to assess heart rate (HR) and blood pressure (BP) are intrusive and can affect results in sensory analysis of food as participants are aware of the sensors. This paper aims to validate a non-contact method to measure HR using the photoplethysmography (PPG) technique and to develop models to predict the real HR and BP based on raw video analysis (RVA) with an example application in chocolate consumption using machine learning (ML). The RVA used a computer vision algorithm based on luminosity changes on the different RGB color channels using three face-regions (forehead and both cheeks). To validate the proposed method and ML models, a home oscillometric monitor and a finger sensor were used. Results showed high correlations with the G color channel (R² = 0.83). Two ML models were developed using three face-regions: (i) Model 1 to predict HR and BP using the RVA outputs with R = 0.85 and (ii) Model 2 based on time-series prediction with HR, magnitude and luminosity from RVA inputs to HR values every second with R = 0.97. An application for the sensory analysis of chocolate showed significant correlations between changes in HR and BP with chocolate hardness and purchase intention.
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Affiliation(s)
- Claudia Gonzalez Viejo
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
| | - Sigfredo Fuentes
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
| | - Damir D Torrico
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
| | - Frank R Dunshea
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
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Al-Naji A, Chahl J. Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2017; 5:1900510. [PMID: 29043113 PMCID: PMC5642312 DOI: 10.1109/jtehm.2017.2757485] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 09/20/2017] [Accepted: 09/22/2017] [Indexed: 11/09/2022]
Abstract
Most existing non-contact monitoring systems are limited to detecting physiological signs from a single subject at a time. Still, another challenge facing these systems is that they are prone to noise artifacts resulting from motion of subjects, facial expressions, talking, skin tone, and illumination variations. This paper proposes an efficient non-contact system based on a digital camera to track the cardiorespiratory signal from a number of subjects (up to six persons) at the same time with a new method for noise artifact removal. The proposed system relied on the physiological and physical effects as a result of the activity of the cardiovascular and respiratory systems, such as skin color changes and head motion. Since these effects are imperceptible to the human eye and highly affected by the noise variations, we used advanced signal and video processing techniques, including developing video magnification technique, complete ensemble empirical mode decomposition with adaptive noise, and canonical correlation analysis to extract the heart rate and respiratory rate from multiple subjects under the noise artifact assumptions. The experimental results of the proposed system had a significant correlation (Pearson's correlation coefficient = 0.9994, Spearman correlation coefficient = 0.9987, and root mean square error = 0.32) when compared with the conventional contact methods (pulse oximeter and piezorespiratory belt), which makes the proposed system a promising candidate for novel applications.
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Affiliation(s)
- Ali Al-Naji
- School of EngineeringUniversity of South AustraliaMawson LakesSA5095Australia
- Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10022Iraq
| | - Javaan Chahl
- School of EngineeringUniversity of South AustraliaMawson LakesSA5095Australia
- Joint and Operations Analysis DivisionDefence Science and Technology GroupMelbourneVIC3207Australia
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Al-Naji A, Gibson K, Chahl J. Remote sensing of physiological signs using a machine vision system. J Med Eng Technol 2017; 41:396-405. [DOI: 10.1080/03091902.2017.1313326] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Ali Al-Naji
- School of Engineering, University of South Australia, Mawson Lakes, Australia
- Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
| | - Kim Gibson
- School of Nursing and Midwifery, University of South Australia, Adelaide, Australia
| | - Javaan Chahl
- School of Engineering, University of South Australia, Mawson Lakes, Australia
- Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, Australia
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Al-Naji A, Gibson K, Lee SH, Chahl J. Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2017; 17:E286. [PMID: 28165382 PMCID: PMC5336086 DOI: 10.3390/s17020286] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 01/27/2017] [Accepted: 01/30/2017] [Indexed: 11/17/2022]
Abstract
The objective of this study was to design a non-invasive system for the observation of respiratory rates and detection of apnoea using analysis of real time image sequences captured in any given sleep position and under any light conditions (even in dark environments). A Microsoft Kinect sensor was used to visualize the variations in the thorax and abdomen from the respiratory rhythm. These variations were magnified, analyzed and detected at a distance of 2.5 m from the subject. A modified motion magnification system and frame subtraction technique were used to identify breathing movements by detecting rapid motion areas in the magnified frame sequences. The experimental results on a set of video data from five subjects (3 h for each subject) showed that our monitoring system can accurately measure respiratory rate and therefore detect apnoea in infants and young children. The proposed system is feasible, accurate, safe and low computational complexity, making it an efficient alternative for non-contact home sleep monitoring systems and advancing health care applications.
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Affiliation(s)
- Ali Al-Naji
- School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia.
- Electrical Engineering Technical College, Middle Technical University, Al Doura 10022, Baghdad, Iraq.
| | - Kim Gibson
- School of Nursing and Midwifery, University of South Australia, Adelaide, SA 5001, Australia.
| | - Sang-Heon Lee
- School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia.
| | - Javaan Chahl
- School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia.
- Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, Victoria 3207, Australia.
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