Development and external validation of a Nomogram to predict obstructive sleep apnea in Children.

Unlabelled: To develop a nomogram model to predict obstructive sleep apnea in children and perform an external validation. 864 children who underwent polysomnography at our hospital were randomly assigned to a training cohort and an internal validation cohort (7:3) and 292 children at another hospital were enrolled for external validation. Logistic regression analyses were performed to explore the risk factors for obstructive sleep apnea.

Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments.

Wearable tesla valve-based sweat collection device for sweat colorimetric analysis.

Sweat sensor has become one of the most important developing directions of in vitro wearable diagnostic device in recent years. Stable sweat collecting device is the key to realize sweat component analysis. In order to ensure that the collected sweat is not subject to component analysis errors caused by evaporation or environmental pollution, mechanical micro-valves were adopted for microfluidic sweat collection devices to realize sealed storage of sweat.