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Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards
Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of t...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471584/ https://www.ncbi.nlm.nih.gov/pubmed/32885290 http://dx.doi.org/10.1007/s10916-020-01653-z |
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author | Xu, Haoran Li, Peiyao Yang, Zhicheng Liu, Xiaoli Wang, Zhao Yan, Wei He, Maoqing Chu, Wenya She, Yingjia Li, Yuzhu Cao, Desen Yan, Muyang Zhang, Zhengbo |
author_facet | Xu, Haoran Li, Peiyao Yang, Zhicheng Liu, Xiaoli Wang, Zhao Yan, Wei He, Maoqing Chu, Wenya She, Yingjia Li, Yuzhu Cao, Desen Yan, Muyang Zhang, Zhengbo |
author_sort | Xu, Haoran |
collection | PubMed |
description | Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: −7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data. |
format | Online Article Text |
id | pubmed-7471584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-74715842020-09-04 Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards Xu, Haoran Li, Peiyao Yang, Zhicheng Liu, Xiaoli Wang, Zhao Yan, Wei He, Maoqing Chu, Wenya She, Yingjia Li, Yuzhu Cao, Desen Yan, Muyang Zhang, Zhengbo J Med Syst Mobile & Wireless Health Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: −7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data. Springer US 2020-09-04 2020 /pmc/articles/PMC7471584/ /pubmed/32885290 http://dx.doi.org/10.1007/s10916-020-01653-z Text en © Springer Science+Business Media, LLC, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Mobile & Wireless Health Xu, Haoran Li, Peiyao Yang, Zhicheng Liu, Xiaoli Wang, Zhao Yan, Wei He, Maoqing Chu, Wenya She, Yingjia Li, Yuzhu Cao, Desen Yan, Muyang Zhang, Zhengbo Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards |
title | Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards |
title_full | Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards |
title_fullStr | Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards |
title_full_unstemmed | Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards |
title_short | Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards |
title_sort | construction and application of a medical-grade wireless monitoring system for physiological signals at general wards |
topic | Mobile & Wireless Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471584/ https://www.ncbi.nlm.nih.gov/pubmed/32885290 http://dx.doi.org/10.1007/s10916-020-01653-z |
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