IoT-Enabled Respiratory Pattern Monitoring in Critical Care: A Real-Time Recurrent Neural Network Approach
Critical in intensive care units (ICUs), monitoring respiratory patterns is essential for diagnosing respiratory distress early and treating patients with severe illnesses properly. Conventional monitoring techniques may not be able to capture all aspects of respiratory dynamics in real-time. This r...
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Published in | Communications and Signal Processing, International Conference on pp. 508 - 513 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
12.04.2024
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Subjects | |
Online Access | Get full text |
ISSN | 2836-1873 |
DOI | 10.1109/ICCSP60870.2024.10543359 |
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Abstract | Critical in intensive care units (ICUs), monitoring respiratory patterns is essential for diagnosing respiratory distress early and treating patients with severe illnesses properly. Conventional monitoring techniques may not be able to capture all aspects of respiratory dynamics in real-time. This research presents a real-time recurrent neural network (RNN) respiratory pattern monitoring system that can be integrated with the Internet of Things (IoT). Tidal volume, respiratory rate, and inspiratory and expiratory flow patterns are just a few of the respiratory characteristics that may be remotely monitored using our system IoT sensors that continuously gather data. An RNN model learns patterns in the time series of respiratory signals and gives instantaneous feedback on the patient's condition based on the processed data. It demonstrates that our approach successfully detects aberrant breathing patterns by evaluating its performance using data obtained from critical care patients. Compared to more conventional ways of monitoring, the proposed system has several benefits, such as the ability to identify respiratory problems early on, analyze data in real time, and provide continuous monitoring. Improving patient outcomes and quality of treatment in critical care settings may potentially provide immediate insights into respiratory dynamics. |
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AbstractList | Critical in intensive care units (ICUs), monitoring respiratory patterns is essential for diagnosing respiratory distress early and treating patients with severe illnesses properly. Conventional monitoring techniques may not be able to capture all aspects of respiratory dynamics in real-time. This research presents a real-time recurrent neural network (RNN) respiratory pattern monitoring system that can be integrated with the Internet of Things (IoT). Tidal volume, respiratory rate, and inspiratory and expiratory flow patterns are just a few of the respiratory characteristics that may be remotely monitored using our system IoT sensors that continuously gather data. An RNN model learns patterns in the time series of respiratory signals and gives instantaneous feedback on the patient's condition based on the processed data. It demonstrates that our approach successfully detects aberrant breathing patterns by evaluating its performance using data obtained from critical care patients. Compared to more conventional ways of monitoring, the proposed system has several benefits, such as the ability to identify respiratory problems early on, analyze data in real time, and provide continuous monitoring. Improving patient outcomes and quality of treatment in critical care settings may potentially provide immediate insights into respiratory dynamics. |
Author | Ilampiray, P Kaliammal, N. Srinivasan, C. Saravanan, S.T. Rajarajan, S. Kalaivani, R. |
Author_xml | – sequence: 1 givenname: S. surname: Rajarajan fullname: Rajarajan, S. email: rajarajan.ece@sairamit.edu.in organization: Sri Sai Ram Institute of Technology,Department of Electronics and Communication Engineering,Chennai,Tamil Nadu,India – sequence: 2 givenname: R. surname: Kalaivani fullname: Kalaivani, R. email: kalairamesh81@gmail.com organization: MIT College of Arts and Science for Women Musiri,Trichy,Tamil Nadu,India – sequence: 3 givenname: N. surname: Kaliammal fullname: Kaliammal, N. email: kaliammal.nagureddiar@gmail.com organization: Muthayammal Engineering College, (Autonomous),Department of Electronics and Communication Engineering,Rasipuram, Namakkal,Tamil Nadu,India – sequence: 4 givenname: S.T. surname: Saravanan fullname: Saravanan, S.T. email: kannansts@gmail.com organization: Sphoorthy Engineering College,Department of CSE-Cyber Security,Hyderabad,Telangana,India – sequence: 5 givenname: P surname: Ilampiray fullname: Ilampiray, P email: iyp.cse@rmkec.ac.in organization: R.M.K. Engineering College,Department of Computer Science and Engineering,Chennai,Tamil Nadu,India – sequence: 6 givenname: C. surname: Srinivasan fullname: Srinivasan, C. email: srinivasanchelliah@gmail.com organization: Saveetha Institute of Medical and Technical Sciences, Saveetha University,Saveetha School of Engineering,Department of Computer Science and Engineering,Chennai,Tamil Nadu,India |
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Snippet | Critical in intensive care units (ICUs), monitoring respiratory patterns is essential for diagnosing respiratory distress early and treating patients with... |
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SubjectTerms | Data Analysis Healthcare Intervention IoT Technology Medical services Neural Network Architecture Proactive Healthcare Real-time systems Recurrent neural networks Signal processing Technological innovation Time series analysis Training |
Title | IoT-Enabled Respiratory Pattern Monitoring in Critical Care: A Real-Time Recurrent Neural Network Approach |
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