10 citations · 20 across the 6 of their papers we have counts for
6 papers · 1 filter
A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea
Stein Kristiansen, Konstantinos Nikolaidis, Thomas Plagemann +11
Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via poly…
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
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 t…
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen +2
Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as…
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical…
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel +3
Supervised machine learning applications in the health domain often face the problem of insufficient training datasets. The quantity of labelled data is small due to privacy concer…
Learning from Higher-Layer Feature Visualizations
Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel +1
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 kno…