Normalization of the Effect of Sampling Rate on the Algorithmic Complexity of Electroencephalograph for Evaluation of Depth of Anesthesia

J.-Y. Li, T.-S. Kuo, and F.-S. Jaw (Taiwan)


Signal Processing, Algorithmic complexity, Sampling rate, Anesthesia, EEG


In this study, a method for normalization of the algorithmic complexity of electroencephalograph (EEG) is proposed. The effect of sampling rate on the algorithmic complexity and the feasibility of the proposed method is demonstrated with EEGs digitized at different sampling rates. Furthermore, the application of this method on evaluation of depth of anesthesia is presented with EEGs recorded in different states of anesthesia. The results suggest that normalization of the effect of sampling rate is indispensable and the proposed method could be helpful for researchers using the algorithmic complexity of EEG to evaluate the depth of anesthesia.

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