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Data-Driven Multimodal Sleep Apnea Events Detection.
- Source :
-
Journal of Medical Systems . Jul2016, Vol. 40 Issue 7, p1-7. 7p. - Publication Year :
- 2016
-
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]
Details
- Language :
- English
- ISSN :
- 01485598
- Volume :
- 40
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Journal of Medical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 115925381
- Full Text :
- https://doi.org/10.1007/s10916-016-0520-7