eprintid: 6551 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/65/51 datestamp: 2023-03-28 23:30:08 lastmod: 2023-03-28 23:30:09 status_changed: 2023-03-28 23:30:08 type: article metadata_visibility: show creators_name: Siddiqui, Hafeez-Ur-Rehman creators_name: Raza, Ali creators_name: Saleem, Adil Ali creators_name: Rustam, Furqan creators_name: Díez, Isabel de la Torre creators_name: Gavilanes Aray, Daniel creators_name: Lipari, Vivian creators_name: Ashraf, Imran creators_name: Dudley, Sandra creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: daniel.gavilanes@uneatlantico.es creators_id: vivian.lipari@uneatlantico.es creators_id: creators_id: title: An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: chronic obstructive pulmonary disease; machine learning; ultra wideband radar; feature engineering abstract: Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage. date: 2023 publication: Diagnostics volume: 13 number: 6 pagerange: 1096 id_number: doi:10.3390/diagnostics13061096 refereed: TRUE issn: 2075-4418 official_url: http://doi.org/10.3390/diagnostics13061096 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage. metadata Siddiqui, Hafeez-Ur-Rehman; Raza, Ali; Saleem, Adil Ali; Rustam, Furqan; Díez, Isabel de la Torre; Gavilanes Aray, Daniel; Lipari, Vivian; Ashraf, Imran y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, vivian.lipari@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features. Diagnostics, 13 (6). p. 1096. ISSN 2075-4418 document_url: http://repositorio.uneatlantico.es/id/eprint/6551/1/diagnostics-13-01096.pdf