Wearable Devices for Early Warning of Acute Exacerbation in Chronic Obstructive Pulmonary Disease Patients

Chun Chieh Hsiao, Cai Ying Chu, Ren Guey Lee, Jer Hwa Chang, Chwan Lu Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of chronic diseases and deaths worldwide. When acute exacerbation of COPD (AECOPD) occurs, the frequency and severity of malignant attacks are highly correlated with the mortality rate. The purpose of this study is to use wearable devices to collect the physiological parameters of patients for early warning and prevention of complications of possible AECOPD attacks in the future. The subjects used wearable devices to measure Heart Rate Variability (HRV) at home. Physiological data and health assessment scales of 13 COPD patients were collected during the 6-month study period. According to the scale responses, the severity of the condition was classified into mild AE and no AE. If the subject needed emergency medical treatment due to COPD, it was classified as AE. With the scale classification method, a machine-learning Random Forest (RF) algorithm is used to predict the occurrence of AECOPD in the next 7 days, so as to prevent the deterioration of the disease in advance. The results of the study show that the accuracy of the model is more than 92% according to different classification methods, and using the mixed-parameter model as a feature for the prediction can improve the sensitivity of the original warning mechanism. In order to provide predictive results to the nursing staff at any time, the user interface of our system would transmit a warning message to remind the nursing staff to ensure early medical intervention for patients to avoid the occurrence of AECOPD.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Systems, Man, and Cybernetics
Subtitle of host publicationImproving the Quality of Life, SMC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4513-4518
Number of pages6
ISBN (Electronic)9798350337020
DOIs
Publication statusPublished - Oct 2023
Event2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 - Hybrid, Honolulu, United States
Duration: Oct 1 2023Oct 4 2023

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Country/TerritoryUnited States
CityHybrid, Honolulu
Period10/1/2310/4/23

Keywords

  • COPD
  • Heart Rate Variability
  • Machine Learning
  • Telecare
  • Wearable Devices

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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