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...

Full description

Saved in:
Bibliographic Details
Published inCommunications and Signal Processing, International Conference on pp. 508 - 513
Main Authors Rajarajan, S., Kalaivani, R., Kaliammal, N., Saravanan, S.T., Ilampiray, P, Srinivasan, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.04.2024
Subjects
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
Online AccessGet full text
ISSN2836-1873
DOI10.1109/ICCSP60870.2024.10543359

Cover

More Information
Summary: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.
ISSN:2836-1873
DOI:10.1109/ICCSP60870.2024.10543359