Hypopnea refers to the state in which insufficient alveolar ventilation during night sleep decreases the respiratory airflow by more than 50% of the airflow. However, sleep apnea is a more serious respiratory event, such as complete cessation of respiratory airflow for 10 seconds. The occurrence of hypopnea is a precursor to the occurrence of apnea events and the two are closely connected. In this paper, we propose a method based on the combination of discrete wavelet transform and approximate entropy of EEG signals to detect sleep apnea and hypopnea events. For this purpose, first, data preprocessing is performed on the EEG record data set obtained from Tianjin Chest Hospital, and then infinite impulse response (IIR) Butterworth bandpass filter is used to decompose the data into delta, theta, alpha, beta and gamma. Second, descriptive features are extracted based on sub-bands discrete wavelet transform such as the approximate entropy of high-frequency coefficients. Third, the features are filtered based on Support Vector Machine (SVM) recursive elimination. Finally, several machine learning algorithms including SVM, K-Nearest Neighbor (KNN) and Random forest (RF) are employed to identify the occurrence of sleep hypopnea-apnea events. The highest accuracy rate reached 94.33%, the sensitivity reached 93.10%, and the specificity reached 95.07%. The obtained results validate that the proposed method is an effective and practical diagnostic method to detect the occurrence of hypopnea-apnea events.

Magnetic induction tomography (MIT) is a non-invasive and non-contact imaging technology, which can be used in medical diagnosis by reconstructing the electrical distribution of biological tissues. Unlike other large medical imaging equipment, the device of MIT is with small size and low cost. The theoretical basis of MIT is by measuring the phase difference of magnetic flux density generated around the imaging objects, analyzing the eddy current distribution, and then using the reconstruction algorithms to obtain the electrical characteristic distribution of the object. This review introduces the development of imaging systems and the reconstruction algorithms of MIT as a medical assisted diagnostic technology, including the optimal design of the sensors, the excitation methods of the system, the calculation methods of the eddy current, and the improved methods of different reconstruction algorithms.

Background: The prevalence of chronic coronary heart diseases (CHDs) increases with age in the elderly, which represents one of the top-ranked causes of death and disease burden. Methods: This study aimed to investigate the management efficiency of telemedicine based on the remote multiparameter monitoring in elderly patients with CHD. A total of 1248 elderly patients diagnosed with CHD were enrolled. The subjects were randomly divided into two groups, wearable equipment-based telemedical management (WTM) group and traditional follow-up management (TFM) group. Face-to-face clinical interview at least once every 2 months was required in TFM group to collect the medical records. Patients in WTM group were provided with wearable equipment to complete remote monitoring, real-time alerts and health intervention via virtual consultations and remote medication recommendations. Results: The mean age of patients in WTM group and TFM group was 71.1 (68.0–82.0) years and 71.0 (68.0–81.0) years, respectively‏. After a 12-month management, patients in WTM group presented a lower occurrence of hospitalisation (HR 0.59, 95% CI=0.47 to 0.73, p<0.0001) and major adverse cardiac events (HR 0.60, 95% CI=0.44 to 0.82, p=0.0012) compared with patients in TFM group. Conclusion: The multiparameter telemedical management could help with the out-of-hospital management and reduce the incidence of rehospitalisation in elderly patients with CHD.

Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are of paramount significance in prolonging the life of patients. Magnetic Detection Electrical Impedance Tomography (MDEIT) based on the Convolutional Neural Network (CNN), which aims to realize non-invasive, high resolution detection of breast tumors, is proposed. First, the MDEIT forward problem of the coronal and horizontal planes of the breast was simulated and solved using the Finite Element Method to obtain sample datasets of different lesions. Then, the CNN was built and trained to predict the conductivity distribution in different orientations of the breast model. Finally, noise and phantom experiments were performed in order to assess the anti-noise performance of the CNN algorithm and its feasibility of detecting breast tumors in practical applications. The simulation results showed that the reconstruction relative error with the CNN algorithm can be reduced to 10%, in comparison with the truncated singular value decomposition algorithm and back propagation algorithm. The CNN algorithm had better stability in the anti-noise performance test. When the noise of 60 dB was added, the shape of the breast tumor could still be restored by the CNN algorithm. The phantom experimental results showed that through the CNN based reconstruction algorithm, the reconstruction conductivity distribution image was legible and the position of the breast tumor could be determined. It is reasonable to conclude that the MDEIT reconstruction method proposed in this study has practical importance for the early and non-invasive detection of breast tumors.