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The K-complex is a transient electroencephalogram (eeg, brain activity) waveform that contributes to sleep stage scoring. An automated detection of K-complexes is an important component of sleep stage monitoring. This automation is difficult due to the stochastic nature of brain signals, presence of noise, complexity, and extreme size of data. We develop an optimization model, based on solving a sequence of linear least squares problems, to extract key features of eeg signals. The proposed approach significantly reduces the dimension of the problem and the computational time while the classification accuracy is enhanced in most cases. Numerical results show that this procedure is efficient in detecting K-complexes.

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