Melekoglu Engin, Kocabicak Umit, Uçar Muhammed Kürşad, Bilgin Cahit, Bozkurt Mehmet Recep, Cunkas Mehmet
Computer Engineering, Sakarya University, Sakarya, Turkey.
Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey.
PeerJ Comput Sci. 2022 Dec 19;8:e1188. doi: 10.7717/peerj-cs.1188. eCollection 2022.
Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD.
During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance.
This improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase.
According to the findings, PPG-based COPD prediction models are suitable for usage in practice.
慢性阻塞性肺疾病（COPD）是全球及我国的一个主要公共卫生问题，由于对该疾病认识不足以及缺乏必要的预防措施，其发病率持续上升。COPD是肺部肺泡堵塞的结果；它是一种持续性疾病，会导致呼吸困难、咳嗽和呼吸急促。COPD的特征是呼吸体征和症状以及气流受限，这是由于大量接触有害颗粒和气体导致气道和肺泡异常所致。用于诊断COPD的肺活量测定试验（呼吸测量试验）在前往医院时存在困难，尤其是对于残疾患者、晚期疾病患者和儿童。为了便于诊断治疗并预防这些问题，经评估认为在COPD疾病诊断中使用光电容积脉搏波描记法（PPG）信号将有助于简化和加速诊断过程，并使其更便于监测。PPG信号包括许多成分，包括与心脏活动相关的动脉血容量变化、改变PPG信号的静脉血容量波动、显示组织光学特性的直流（DC）成分以及身体中的适度能量变化。PPG通常用于脉搏血氧仪，它照亮皮肤并测量光吸收的变化。随着每次心跳出现的PPG是一个易于测量的信号。通过机器学习对PPG信号进行建模以预测COPD。
在研究过程中，对PPG信号进行去噪处理，得到了具有三个PPG低频带的全新PPG信号。从这四个信号中的每一个提取了25个特征。总共提取了100个特征。此外，体重、身高和年龄也被用作特征。在特征选择过程中，我们采用了Fisher方法。使用该方法的目的是提高性能。
这种改进的PPG预测模型对所有个体的准确率为0.95性能值。特征选择算法中使用的分类算法有助于提高性能。
根据研究结果，基于PPG的COPD预测模型适用于实际应用。
