Department of Computer Science & INSPIRES, University of Lleida, Jaume II 69, 25001 Lleida, Spain.
CIMNE, Building C1, North Campus, UPC, Gran Capità, 08034 Barcelona, Spain.
Sensors (Basel). 2022 Feb 1;22(3):1106. doi: 10.3390/s22031106.
Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person's lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users' expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.
肺量计是监测呼吸系统疾病患者的重要设备。这些仪器主要只在医院使用，这带来了许多不便。这限制了它们的使用，进而限制了对患者的监测。研究工作的重点是提供肺量计的数字替代方案。虽然准确性较低，但作者声称它们更便宜，并且在任何给定时间和地点都可以由更多的人使用。为了进一步推广肺量计的使用，我们还希望提供易于使用的肺容量指标，而不是传统的肺量计指标。本研究的主要目的，也是本研究的主要贡献，是通过机器学习方法分析呼气的特性，从而获得一个人的肺龄。为了进行这项研究，我们使用了 188 个吹气声音样本。这些样本来自 91 名男性（48.4%）和 97 名女性（51.6%），年龄在 17 岁至 67 岁之间。总共使用了 42 个肺量计和类似频率的特征，包括性别。应用于最显著特征的传统语音识别机器学习算法被用于这项研究。我们发现，在不区分性别的情况下，最好的分类算法是二次线性判别算法。当将语料库按 5 岁连续年龄组划分时，发现准确率、敏感度和特异性分别为 94.69%、94.45%和 99.45%。成功检测到了允许用户根据其相应的 5 岁年龄组进行分类的用户呼气音频中的特征。我们的方法可以成为移动设备上检测肺部异常或疾病的可靠工具。
