Chronic obstructive pulmonary disease (COPD) is a serious inflammatory lung
disease affecting millions of people around the world. Due to an obstructed
airflow from the lungs, it also becomes manifest in patients' vocal behaviour.
Of particular importance is the detection of an exacerbation episode, which
marks an acute phase and often requires hospitalisation and treatment. Previous
work has shown that it is possible to distinguish between a pre- and a
post-treatment state using automatic analysis of read speech. In this
contribution, we examine whether sustained vowels can provide a complementary
lens for telling apart these two states. Using a cohort of 50 patients, we show
that the inclusion of sustained vowels can improve performance to up to 79\%
unweighted average recall, from a 71\% baseline using read speech. We further
identify and interpret the most important acoustic features that characterise
the manifestation of COPD in sustained vowels.

使用 50 名患者的研究，我们发现使用持续元音可以提高对慢性阻塞性肺病在语音中表现的识别准确率，从 71％的基准提高到最高 79％的无权重平均召回率，并鉴定和解释了表征 COPD 的最重要的声学特征。

持续元音在 COPD 预处理与后处理分类中的应用

Sustained Vowels for Pre- vs Post-Treatment COPD Classification

Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung
condition that causes airflow obstruction. The existing methods can only detect
patients who already have COPD based on obvious features shown in the spirogram
(In this article, the spirogram specifically involves measuring Volume-Flow
curve time series). Early prediction of COPD risk is vital for monitoring COPD
disease progression, slowing it down, or even preventing its onset. However,
these methods fail to early predict an individual's probability of COPD in the
future based on subtle features in the spirogram. To address this gap, for the
first time, we propose DeepSpiro, a method based on deep learning for early
prediction of future COPD risk. DeepSpiro consists of four parts. First, we
construct Volume-Flow curves guided by Time-Volume instability smoothing
(SpiroSmoother) to enhance the stability of the original Volume-Flow curves
precisely. Second, we extract critical features from the evolution of
varied-length key patches (SpiroEncoder) to capture the key temporal evolution
from original high-dimensional dynamic sequences to a unified low-dimensional
temporal representation. Third, we explain the model based on temporal
attention and heterogeneous feature fusion (SpiroExplainer), which integrates
information from heterogeneous data such as spirogram and demographic
information. Fourth, we predict the risk of COPD based on the evolution of key
patch concavity (SpiroPredictor), enabling accurate prediction of the risk of
disease in high-risk patients who are not yet diagnosed, for up to 1, 2, 3, 4,
5 years, and beyond. We conduct experiments on the UK Biobank dataset. Results
show that DeepSpiro achieves an AUC value of 0.8328 in the task of detecting
COPD. In early prediction tasks, high-risk and low-risk groups show significant
differences in the future, with a p-value of <0.001.

针对慢性阻塞性肺疾病（COPD）的早期风险预测问题，首次提出了基于深度学习的 DeepSpiro 方法，通过构建稳定的体积 - 流量曲线、提取关键特征、解释模型以及基于关键补丁凹陷的风险预测，能够准确预测高风险患者未来 1 至 5 年及更长时间内的 COPD 风险，实验结果表明其 AUC 值为 0.8328。