University of Coimbra Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal.
University of Coimbra Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal.
Comput Methods Programs Biomed. 2023 Oct;240:107720. doi: 10.1016/j.cmpb.2023.107720. Epub 2023 Jul 16.
Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EIT data are available.
In this work, we have assembled the first open-access bimodal database focusing on the differential diagnosis of respiratory diseases (BRACETS: Bimodal Repository of Auscultation Coupled with Electrical Impedance Thoracic Signals). It includes simultaneous recordings of single and multi-channel respiratory sounds and EIT. Furthermore, we have proposed several machine learning-based baseline systems for automatically classifying respiratory diseases in six distinct evaluation tasks using respiratory sound and EIT (A1, A2, A3, B1, B2, B3). These tasks included classifying respiratory diseases at sample and subject levels. The performance of the classification models was evaluated using a 5-fold cross-validation scheme (with subject isolation between folds).
The resulting database consists of 1097 respiratory sounds and 795 EIT recordings acquired from 78 adult subjects in two countries (Portugal and Greece). In the task of automatically classifying respiratory diseases, the baseline classification models have achieved the following average balanced accuracy: Task A1 - 77.9±13.1%; Task A2 - 51.6±9.7%; Task A3 - 38.6±13.1%; Task B1 - 90.0±22.4%; Task B2 - 61.4±11.8%; Task B3 - 50.8±10.6%.
The creation of this database and its public release will aid the research community in developing automated methodologies to assess and monitor respiratory function, and it might serve as a benchmark in the field of digital medicine for managing respiratory diseases. Moreover, it could pave the way for creating multi-modal robust approaches for that same purpose.
呼吸疾病是全球发病率和死亡率的主要原因之一，对社会和医疗系统造成了巨大的负担。在过去几十年中，人们对呼吸声音和电阻抗断层成像（EIT）的自动分析越来越感兴趣。然而，目前还没有同时包含呼吸声音和 EIT 数据的公开可用数据库。
在这项工作中，我们首次组装了一个开放获取的双模态数据库，专注于呼吸疾病的鉴别诊断（BRACETS：听诊与电阻抗胸部信号的双模态存储库）。它包括单通道和多通道呼吸声音和 EIT 的同步记录。此外，我们还提出了几种基于机器学习的基线系统，用于在六个不同的评估任务中使用呼吸声音和 EIT 自动分类呼吸疾病（A1、A2、A3、B1、B2、B3）。这些任务包括在样本和个体水平上分类呼吸疾病。使用 5 折交叉验证方案（折间个体隔离）评估分类模型的性能。
该数据库由来自两个国家（葡萄牙和希腊）的 78 位成年个体的 1097 次呼吸声音和 795 次 EIT 记录组成。在自动分类呼吸疾病的任务中，基线分类模型的平均平衡准确率为：任务 A1-77.9±13.1%；任务 A2-51.6±9.7%；任务 A3-38.6±13.1%；任务 B1-90.0±22.4%；任务 B2-61.4±11.8%；任务 B3-50.8±10.6%。
该数据库的创建和公开将有助于研究人员开发评估和监测呼吸功能的自动化方法，并且可以作为数字医学领域管理呼吸疾病的基准。此外，它还可以为实现同一目的的多模态稳健方法铺平道路。
