1. Data-Driven Multimodal Sleep Apnea Events Detection.
- Author
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Rutkowski, Tomasz
- Subjects
- *
ALGORITHMS , *DISCRIMINANT analysis , *ELECTROENCEPHALOGRAPHY , *NONPARAMETRIC statistics , *SIGNAL processing , *POLYSOMNOGRAPHY , *SLEEP apnea syndromes , *BRAIN-computer interfaces , *BRAIN waves , *DESCRIPTIVE statistics , *MANN Whitney U Test , *DIAGNOSIS - Abstract
A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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