Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan.
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; Department of Electronics and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Indonesia.
Comput Biol Med. 2022 Sep;148:105913. doi: 10.1016/j.compbiomed.2022.105913. Epub 2022 Aug 2.
As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's leading cause of cancer death. One of the methods is to analyze the Volatile Organic Compounds (VOCs) in exhaled breath using electronic noses (E-noses), which have become emerging tools for analyzing breath because of their potential and promising technology for diagnosing. However, the signal processing of the E-Nose sensor becomes vital in exposing information about the subject condition, which most researchers strive to accomplish. We proposed a Convolutional Neural Network (CNN) architecture to classify COPD in smokers and non-smokers, healthy subjects, and smokers from E-Nose signals to contribute to this field. Two models were constructed following E-Nose signal processing state-of-the-arts. One was by combined feature extraction and classifier, and the second was by CNN, which directly processed the raw signal. In addition, various feature extraction and classifier (Machine Learning and CNN) used in prior research were investigated. Using 3K and 5K Fold cross-validation results demonstrated that our proposed models outperformed in Kernel Principal Component Analysis (KPCA) with Fx-ConvNet and Pure-ConvNet. They all reached maximum F1-Score with zero standard deviation values indicating a consistent result. Further experiments also showed that KPCA contributed to the increasing performance of some classifiers with average F1-Score 0.933 and 0.068 as standard deviation values.
作为最可靠和重要的指标之一，慢性阻塞性肺疾病（COPD）成为肺癌早期检测的有力预测指标，肺癌是全球癌症死亡的主要原因。方法之一是使用电子鼻（E-nose）分析呼气中的挥发性有机化合物（VOCs），由于其在诊断方面具有潜力和有前途的技术，电子鼻已成为分析呼吸的新兴工具。然而，E-Nose 传感器的信号处理对于揭示有关主体状况的信息至关重要，这是大多数研究人员努力实现的目标。我们提出了一种卷积神经网络（CNN）架构，用于对吸烟者和非吸烟者、健康受试者和吸烟者的 E-Nose 信号进行 COPD 分类，为该领域做出贡献。根据 E-Nose 信号处理的最新技术构建了两个模型。一个是通过组合特征提取和分类器构建的，另一个是通过直接处理原始信号的 CNN 构建的。此外，还研究了先前研究中使用的各种特征提取和分类器（机器学习和 CNN）。使用 3K 和 5K 折叠交叉验证结果表明，我们提出的模型在使用 Fx-ConvNet 和 Pure-ConvNet 的核主成分分析（KPCA）中表现出色。它们都达到了最大 F1 分数，零标准差值表明结果一致。进一步的实验还表明，KPCA 有助于提高某些分类器的性能，平均 F1 分数为 0.933，标准差为 0.068。
