Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.

背景与目的呼吸障碍的自动检测在呼吸系统疾病的早期信号化中起着重要作用。测量方法可以基于心电图 (ECG)、声音、血氧测定或呼吸分析。然而，这些方法需要将设备放置在人体上，否则它们容易受到环境影响的干扰。为了解决这些问题，我们提出了一种心脏收缩机械触发器，用于通过心脏收缩的机械测量不显眼地检测呼吸系统疾病。我们设计了一种新颖的方法来完全根据测量的机械信号计算这种机械触发，而不使用心电图。方法该方法是根据信号对所谓的欧几里得弧长进行的内置计算。与之前的研究相比，该系统不需要任何附加到人身上的设备。这是通过将张力计放在床上来实现的。来自传感器的数据通过 Cartan 曲率方法融合到卷积神经网络 (CNN) 分类器的逐搏矢量输入。结果共收集到2281个无序呼吸样本和5130个正常呼吸样本进行分析。使用10折交叉验证的实验表明准确度、灵敏度和特异性分别达到96.37%、92.46%和98.11%的值。结论通过检测方法，该系统为呼吸相关健康问题的完全不显眼的诊断提供了一种新方法。建议的解决方案可以有效地部署在所有临床或家庭环境中。