RT info:eu-repo/semantics/article
T1 Respiration-based COPD detection using UWB radar incorporation with machine learning
A1 Siddiqui, Hafeez Ur Rehman
A1 Saleem, Adil Ali
A1 Bashir, Imran
A1 Zafar, Kainat
A1 Rustam, Furqan
A1 Torre Díez, Isabel de la
A1 Dudley, Sandra
A1 Ashraf, Imran
K1 Lungs - Diseases, Obstructive
K1 Respiratory organs - Diseases - Diagnosis
K1 Pulmones - Enfermedades
K1 Organos respiratorios - Enfermedades
K1 Neumología
K1 Radar
K1 Respiración
K1 3205.08 Enfermedades Pulmonares
K1 3325 Tecnología de las Telecomunicaciones
AB COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach.
PB MDPI
SN 2079-9292
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61312
UL https://uvadoc.uva.es/handle/10324/61312
LA eng
NO Electronics, 2022, Vol. 11, Nº. 18, 2875
NO Producción Científica
DS UVaDOC
RD 19-nov-2024