Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes in the depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA was quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared with sample entropy (SampEn), detrended fluctuation analysis (DFA), and permutation entropy (PE). The performance of quasi-periodicities defined by deceleration capacity and acceleration capacity was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference ( $p <0.05$ ) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared with SampEn, DFA, and PE using expert assessment of conscious level and bispectral index as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are a powerful monitor of DOA and perform more accurate and robust results compared with SampEn, DFA, and PE. The results do provide a valuable reference to researchers in the field of clinical applications.