In this article we present a fusion architecture for the automatic classification of sleep stages. The architecture relies on time and frequency domain features which are processed by dissimilar classifiers. The initial predictions of each classifier are refined by using fusion of the prediction estimations together with temporal contextual information of the electroencephalographic signal. The experimental results showed that the proposed architecture achieved approximately 95% sleep stage classification accuracy, which corresponds to an improvement of 5% comparing to the best performing single classifier.
|Title of host publication||2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 28 Dec 2015|
|Event||15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015 - Belgrade, Serbia|
Duration: 2 Nov 2015 → 4 Nov 2015
|Conference||15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015|
|Period||2/11/15 → 4/11/15|