Siddiqui Hafeez-Ur-Rehman, Raza Ali, Saleem Adil Ali, Rustam Furqan, Díez Isabel de la Torre, Aray Daniel Gavilanes, Lipari Vivian, Ashraf Imran, Dudley Sandra
Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
School of Computer Science, University College Dublin, Dublin 4, D04 V1W8 Dublin, Ireland.
Diagnostics (Basel). 2023 Mar 14;13(6):1096. doi: 10.3390/diagnostics13061096.
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.
慢性阻塞性肺疾病（COPD）是一种严重的慢性疾病，目前是全球第三大常见死因。COPD患者经常经历使人衰弱的症状，如慢性咳嗽、呼吸急促和疲劳。遗憾的是，这种疾病常常在为时已晚之前未被诊断出来，使患者得不到他们急需的治疗。因此，早期检测COPD对于防止肺部进一步受损和提高生活质量至关重要。传统的COPD检测方法通常依赖体格检查和诸如肺活量测定、胸部X光检查、血气测试和基因检测等测试。然而，这些方法可能并不总是准确或易于实施。检测COPD的关键生命体征之一是患者的呼吸频率。然而，为了获得更好的检测结果，同时考虑患者的医学和人口统计学特征至关重要。为了解决这个问题，本研究旨在使用人工智能技术检测COPD患者。为实现这一目标，提出了一种新颖的框架，该框架利用基于超宽带（UWB）雷达的时间和频谱特征来构建机器学习和深度学习模型。这组新的时间和频谱特征是从使用UWB雷达在1.5米距离处无创收集的呼吸数据中提取的。在收集到的数据集上对不同的机器学习和深度学习模型进行训练和测试。研究结果很有前景，COPD检测的准确率高达100%。这意味着所提出的框架有可能通过早期识别COPD患者来挽救生命。应用k折交叉验证技术并与现有最先进的研究进行性能比较来验证其性能，确保结果是稳健和可靠的。该研究中获得的高准确率意味着所提出的框架具有在早期有效检测COPD的潜力。
