Abstract
Sleep episodes are generally classified according to EEG, EMG, ECG, EOG and other signals. Many experts at home and abroad put forward many automatic sleep staging classification methods, however the accuracy of most methods still remain to be improved. This paper firstly improves the initial center of clustering by combining the correlation coefficient and the correlation distance and uses the idea of piecewise function to update the clustering center. Based on the improvement of K-means clustering algorithm, an automatic sleep stage classification algorithm is proposed and is adopted after the wavelet denoising, EEG data feature extraction and spectrum analysis. The experimental results show that the classification accuracy is improved and the sleep automatic staging algorithm is effective by comparison between the experimental results with the artificial markers and the original algorithms.
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Shao, X., Hu, B., Zheng, X. (2017). A Study on Automatic Sleep Stage Classification Based on Clustering Algorithm. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_13
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DOI: https://doi.org/10.1007/978-3-319-70772-3_13
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