Evidence map›Paper›PMID 29961144›Full record

ArticleJournal of medical systems2018

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia.

Quan Liu, Li Ma, Shou-Zen Fan, Maysam F Abbod, Cheng-Wei Lu, Tzu-Yu Lin, Kuo-Kuang Jen, Shang-Ju Wu, Jiann-Shing Shieh

Abstract read
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In one paragraph

Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Machine learning in perioperative medicine: a systematic review.Journal of anesthesia, analgesia and critical care · 2022
    Review
  2. Wireless Cyber-Physical Systems Performance Evaluation through a Graph Database Approach.Journal of computing and information science in engineering · 2021
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Quan LiuSchool of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China.
Li MaSchool of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China.
Shou-Zen FanDepartment of Anesthesiology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan.
Maysam F AbbodDepartment of Electronic and Computer Engineering, Brunel University London, Uxbridge, UB8 3PH, UK.
Cheng-Wei LuDepartment of Mechanical Engineering, and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li, 32003, Taiwan.
Tzu-Yu LinDepartment of Mechanical Engineering, and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li, 32003, Taiwan.
Kuo-Kuang JenNational Chung-Shan Institute of Science and Technology, Taoyuan, Longtan, Taiwan.
Shang-Ju WuNational Chung-Shan Institute of Science and Technology, Taoyuan, Longtan, Taiwan.
Jiann-Shing ShiehDepartment of Mechanical Engineering, and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li, 32003, Taiwan. jsshieh@saturn.yzu.edu.tw.

Funding

National Chung-Shan Institute of Science & Technology in Taiwan CSIST-095-V401National Natural Science Foundation of China 51475342National Natural Science Foundation of China 51675389
6 · The paper itself

Abstract

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Indexed as

AnesthesiaOperating RoomsAnesthesiologyChildElectrocardiographyElectroencephalographyHumansMonitoring, PhysiologicTaiwanAnesthesiaData acquiringFlexibleMulti-operating roomPhysiological medical signalWireless network platform

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.