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Sleep stages classification using wavelettransform & neural networkPublished in
2012
Pages: 71 - 74
In this paper the feature extraction of the EEG Signal is done by computing the Discrete Wavelet Transform. The wavelet transform coefficients compress the number of data points into few features. Various statistics were used to further reduce the dimensionality. The Classification of the EEG sleep stages is done by using neural network which provides more accurate sleep stage classification compared to other techniques. © 2012 IEEE.
Topics: Wavelet transform (60)%60% related to the paper, Discrete wavelet transform (60)%60% related to the paper, Sleep Stages (57)%57% related to the paper and Feature extraction (53)%53% related to the paper
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| Journal | Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 |
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