Sleep Apnea Detection Using Oxygen Saturation During Nocturnal Sleep.

The early detection and treatment of sleep apnea syndrome (SAS) is becoming an important issue in medical practice, as the conventional polysomnography-based method is not only costly, but also demands high expertise from technicians. In this study, we propose a method for diagnosing SAS using noninvasive measurements of oxygen saturation (SpO2), obtained from a pulse oximeter as the input signal, using convolutional neural networks (CNNs). As a result of 10-fold cross-validation, we confirmed that the proposed method yields an 88.

Evaluation of Patients' Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital.

This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not.