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 algorithms sensitivity is 93.95%, and specificity is 84.87%.
Pandeng Zhang Jia Liu Xinyu Wu Xiaochang Liu Qingchun Gao
Shenzhen Institute of Advanced Technology, Shenzhen, China Chinese Academy of Sciences/The Chinese U The Second Affiliated Hospital of Guangzhou Medical College, Guangzhou, China
国际会议
成都
英文
1-4
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）