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...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 10 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146224567&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146224567 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2020 vid: 44 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146224567 146224567 146224567 10.1007/s10916-020-01653-z 146224567 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards. aug: au: 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 affil: Medical School of Chinese PLA, Beijing, China sug: subj: Signal Processing, Computer Assisted Monitoring, Physiologic Hospital Units Wearable Sensors Human Electronic Health Records Machine Learning Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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