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Found 6 papers, 1 papers with code
A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea
Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep.
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
A new model is trained with these labels to generalize reliably despite the label noise.
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access to a classifier trained on the source data is provided.
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
We use sleep monitoring data from both an open and a large closed clinical study and evaluate whether (1) end-users can create and successfully use customized classification models for sleep apnea detection, and (2) the identity of participants in the study is protected.
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
Supervised machine learning applications in the health domain often face the problem of insufficient training datasets.
Learning from Higher-Layer Feature Visualizations
Driven by the goal to enable sleep apnea monitoring and machine learning-based detection at home with small mobile devices, we investigate whether interpretation-based indirect knowledge transfer can be used to create classifiers with acceptable performance.
