Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.
Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.