Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels.

Sleep apnea, a prevalent sleep-related breathing disorder, often remains undiagnosed and untreated in a large patient population due to the need of extensive manual annotations on various physiological signals for clinical diagnosis. Despite the surge of interest in applying machine learning to automate apnea detection, the effectiveness of existing techniques highly relies on strongly supervised learning that requires massive finely labeled training data for sufficiently short time intervals - a requirement often unmet due to the prohibitively high cost of manual labeling in clinical practice. In this article, we incorporate clinical knowledge to establish a weakly supervised deep learning framework for automatically estimating the latent fine-grained apnea severity when only coarse-grained labels indicating apnea presence are available in the training data.

Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection.

Sleep Apnea (SA) is a prevalent sleep disorder with multifaceted etiologies that can have severe consequences for patients. Diagnosing SA traditionally relies on the in-laboratory polysomnogram (PSG), which records various human physiological activities overnight. SA diagnosis involves manual scoring by qualified physicians.

Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.

Unlabelled: To avoid unexpected failures of units in manufacturing systems, failure mode recognition and prognostics are critically important in prognostics health management (PHM). Most existing methods either ignored the effects of various failure modes on remaining useful lifetime (RUL) prediction or implemented failure mode recognition and RUL prediction as two independent tasks, which failed to exploit failure mode information to obtain accurate RUL prediction. In fact, RUL highly depends on failure modes because sensor signals under different failure modes usually present different degradation patterns.

Overexpression of circular RNA circ_001569 indicates poor prognosis in hepatocellular carcinoma and promotes cell growth and metastasis by sponging miR-411-5p and miR-432-5p.

Emerging evidence indicated that abnormally expressed circular RNAs (circRNAs) are critically involved in tumorigenesis and development of several cancers. However, the study relevant to the relationship between circRNAs and hepatocellular carcinoma (HCC) is rare. In this study, the expression of circ_001569 in HCC tissue specimens and cells were determined by qRT-PCR.

An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals.

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis.