Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm

Algorithm 1

ASSC based on improved CEEMDAN and K-means.

	Require:
	The original EEG signal is processed with the wavelet denoising algorithm.
	Ensure:
	The clustering results indicate that EEG signal is divided into different sleep stages.
	(1) Define an N-point EEG epoch .
	(2) Variable is the noise standard deviation; is the number of realizations; is the maximum number of sifting iterations allowed.
	(3) By improved CEEMDAN decomposition, the first mode and the first residual component are obtained, as in formulas (6)–(9).
	(4) for k = 2, …, K do
	(5)  Calculate the k-th IMF component and residual component .
	(6)  Decomposing to achieve the new mode as in formula (11).
	(7) end for
	(8) The initial cluster center is divided.
	(9) According to formulas (16) and (17), the correlation distance between the data points and the density of each point are calculated, and the smallest is used as the first cluster center to obtain set .
	(10) The data remaining in set are allocated to the nearest class according to the distance from the nearest cluster center.
	(11) According to formula (19), the distance is calculated from each point to the center in each class; u and are calculated according to different segment calculations. The smallest is obtained as the new clustering center.
	(12) Recalculate and assign individual sample objects until the cluster center no longer changes.