Literature DB >> 26087508

Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications.

Atena Roshan Fekr, Katarzyna Radecka, Zeljko Zilic.   

Abstract

A reliable long-term monitoring and diagnosis of breath disorders at an early stage provides an improvement of medical act, life expectancy, and quality of life while decreasing the costs of treatment and medical services. Therefore, a real-time unobtrusive monitoring of respiration patterns, as well as breath parameters, is a critical need in medical applications. In this paper, we propose an intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology. The proposed system is designed particularly for the goal of diagnosis and treatment in patients with pathological breathing, e.g., respiratory complications after surgery or sleep disorders. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth low energy, and cloud database. The experiments are conducted with eight subjects and the overall error in respiration rate calculation is obtained 0.29%±0.33% considering SPR-BTA spirometer as the reference. We also introduce a method for tidal volume variability estimation while validated using Pearson correlation. Furthermore, since it is essential to detect the critical events resulted from sudden rise or fall in per breath tidal volume of the patients, we provide a technique to automatically find the accurate threshold values based on each individual breath characteristics. Therefore, the system is able to detect the major changes, precisely by more than 98%, and provide immediate feedback such as sound alarm for round-the-clock respiration monitoring.

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Year:  2015        PMID: 26087508     DOI: 10.1109/JBHI.2015.2445783

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  8 in total

1.  Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning.

Authors:  Rémy Dumond; Steven Gastinger; Hala Abdul Rahman; Alexis Le Faucheur; Patrice Quinton; Haitao Kang; Jacques Prioux
Journal:  Eur J Appl Physiol       Date:  2017-06-13       Impact factor: 3.078

2.  Contactless Monitoring of Breathing Pattern and Thoracoabdominal Asynchronies in Preterm Infants Using Depth Cameras: A Feasibility Study.

Authors:  Valeria Ottaviani; Chiara Veneroni; Raffaele L Dellaca'; Anna Lavizzari; Fabio Mosca; Emanuela Zannin
Journal:  IEEE J Transl Eng Health Med       Date:  2022-03-21

3.  Measuring diaphragm movement and respiratory frequency using a novel ultrasound device in healthy volunteers.

Authors:  Håvard Andreassen Sæverud; Ragnhild Sørum Falk; Adam Dowrick; Morten Eriksen; Sigurd Aarrestad; Ole Henning Skjønsberg
Journal:  J Ultrasound       Date:  2019-11-06

Review 4.  Performance Evaluation of Bluetooth Low Energy: A Systematic Review.

Authors:  Jacopo Tosi; Fabrizio Taffoni; Marco Santacatterina; Roberto Sannino; Domenico Formica
Journal:  Sensors (Basel)       Date:  2017-12-13       Impact factor: 3.576

5.  The Ultrasonic Directional Tidal Breathing Pattern Sensor: Equitable Design Realization Based on Phase Information.

Authors:  Arijit Sinharay; Raj Rakshit; Anwesha Khasnobish; Tapas Chakravarty; Deb Ghosh; Arpan Pal
Journal:  Sensors (Basel)       Date:  2017-08-11       Impact factor: 3.576

6.  Ultrasound Sensors for Diaphragm Motion Tracking: An Application in Non-Invasive Respiratory Monitoring.

Authors:  Amirhossein Shahshahani; Carl Laverdiere; Sharmistha Bhadra; Zeljko Zilic
Journal:  Sensors (Basel)       Date:  2018-08-09       Impact factor: 3.576

Review 7.  The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise.

Authors:  Andrea Nicolò; Carlo Massaroni; Emiliano Schena; Massimo Sacchetti
Journal:  Sensors (Basel)       Date:  2020-11-09       Impact factor: 3.576

8.  Towards breath sensors that are self-powered by design.

Authors:  Lucy Fitzgerald; Luis Lopez Ruiz; Joe Zhu; John Lach; Daniel Quinn
Journal:  R Soc Open Sci       Date:  2022-09-21       Impact factor: 3.653

  8 in total

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