An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features

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.

Morphological features of lung white spots based on the Otsu and Phansalkar thresholding method

“…Danapaquiame et al [17] discussed image processing techniques and analysis related to COVID-19, including image processing techniques, segmentation, diagnosis, and follow-up based on traditional methods to evaluate the entire discussion. Siddiqui et al [18] proposed using the ultra wide-band radar method temporally in the early detection of chronic lung disease. Aswathy et al [19] used nano images enhanced with Gabor filters and histogram equalization in the early detection of lung cancer based on the guaranteed convergence particle swarm optimization (GCPSO) algorithm.…”

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach

“…Recent technological advancements offer promising possibilities for the effective detection of anterior and inferior MI. Ultra-wideband (UWB) is a frequently utilized technology in the monitoring of vital signs [ 14 , 23 , 24 , 25 , 26 , 27 ]. It has several advantageous features over ECG and other technologies.…”

“…UWB radar employs low power levels and high data rates to produce high-bandwidth signals characterized by extremely brief pulse durations. The device’s capacity to emit a million nano-pulses per second enables it to effectively identify and monitor min movements and vibrations, including respiration and cardiac activity [ 26 , 28 , 29 ]. Significantly, the IR-UWB radar system is not dependent on visible light or skin complexion, rendering it suitable for deployment in diverse environmental conditions [ 28 , 29 ].…”

Deep Learning-Based Feature Engineering to Detect Anterior and Inferior Myocardial Infarction Using UWB Radar Data

“…To address these issues, a non-contact approach to accurately classify different forms of MI is required. Ultrawideband (UWB) radar is emerging as a potential non-contact approach for vital sign monitoring and diagnosis [19], [20], [21], [22], [23], [24]. UWB transmissions can generate high-bandwidth signals using very short-duration pulses and can detect and monitor micro-movements and vibrations like breathing and heartbeats [25].…”