A Novel Feature Extraction Method for Signal Quality Assessment of Arterial Blood Pressure for Monitoring Cerebral Autoregulation

In this paper, we proposed a novel method of signal quality assessment of arterial blood pressure for monitoring Cerebral Autoregulation (CA). This method is based on algorithm of signal abnormality index (SAI). Two simple and effective features-end diastole slope sum (EDSS) and slow ejection slope sum (SESS), were proposed to identify abnormal beats from ABP as CA input in real-time. The methods of cumulative distribution function (CDF) and receiver operating characteristic (ROC) analysis were used to select best feature and confirm the parameter of the feature. Using the best feature with SAI model, we can directly estimate the signal quality of ABP in CA assessment. It has been tested in the data of CA assessment experiment and compared to an expert annotator, the algorithm´s sensitivity is 93.95%, and specificity is 84.87%.