Kolozali, Sefki and Chatzidiakou, Lia and Rones, Roderic and Quint, Jennifer K and Kelly, Frank and Barratt, Benjamin (2023) Early Detection of COPD Patients’ Symptoms with Personal Environmental Sensors: A Remote Sensing Framework using Probabilistic Latent Component Analysis with Linear Dynamic Systems. Neural Computing and Applications, 35 (23). pp. 17247-17265. DOI https://doi.org/10.1007/s00521-023-08554-5
Kolozali, Sefki and Chatzidiakou, Lia and Rones, Roderic and Quint, Jennifer K and Kelly, Frank and Barratt, Benjamin (2023) Early Detection of COPD Patients’ Symptoms with Personal Environmental Sensors: A Remote Sensing Framework using Probabilistic Latent Component Analysis with Linear Dynamic Systems. Neural Computing and Applications, 35 (23). pp. 17247-17265. DOI https://doi.org/10.1007/s00521-023-08554-5
Kolozali, Sefki and Chatzidiakou, Lia and Rones, Roderic and Quint, Jennifer K and Kelly, Frank and Barratt, Benjamin (2023) Early Detection of COPD Patients’ Symptoms with Personal Environmental Sensors: A Remote Sensing Framework using Probabilistic Latent Component Analysis with Linear Dynamic Systems. Neural Computing and Applications, 35 (23). pp. 17247-17265. DOI https://doi.org/10.1007/s00521-023-08554-5
Abstract
In this study, we present a cohort study involving 106 COPD patients using portable environmen- tal sensor nodes with attached air pollution sensors and activity-related sensors, as well as daily symptom records and peak flow measurements to monitor patients’ activity and personal expo- sure to air pollution. This is the first study which attempts to predict COPD symptoms based on personal air pollution exposure. We developed a system that can detect COPD patients’ symp- toms one day in advance of symptoms appearing. We proposed using the Probabilistic Latent Component Analysis (PLCA) model based on 3-dimensional and 4-dimensional spectral dictionary tensors for personalised and population monitoring, respectively. The model is combined with Lin- ear Dynamic Systems (LDS) to track the patients’ symptoms. We compared the performance of PLCA and PLCA-LDS models against Random Forest models in the identification of COPD patients’ symptoms, since tree based classifiers were used for remote monitoring of COPD patients in literature. We found that there was a significant difference between the classifiers, symptoms and the per- sonalised versus population factors. Our results show that the proposed PLCA-LDS-3D model outperformed the PLCA and the RF models between 4% and 20% on average. When we used only air pollutants as input, the PLCA-LDS-3D forecasting results in personalised and popula- tion models were 48.67% and 36.33% accuracy for worsening of lung capacity and 38.67% and 19% accuracy for exacerbation of COPD patients’ symptoms, respectively. We have shown that indicators of the quality of an individual’s environment, specifically air pollutants, are as good predictors of the worsening of respiratory symptoms in COPD patients as a direct measurement.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Internet of things (IoT) in Healthcare; Remote monitoring systems; Personal air pollution exposure; Chronic obstructive pulmonary disease (COPD); Probabilistic latent models |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 20 Jul 2023 19:34 |
Last Modified: | 30 Oct 2024 20:58 |
URI: | http://repository.essex.ac.uk/id/eprint/35306 |
Available files
Filename: s00521-023-08554-5.pdf
Licence: Creative Commons: Attribution 4.0