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

Content Provider	IEEE Xplore Digital Library
Author	Pandeng Zhang Jia Liu Xinyu Wu Xiaochang Liu Qingchun Gao
Copyright Year	2010
Abstract	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%.
Starting Page	1
Ending Page	4
File Size	301871
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5515739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hospitals Design methodology Arterial blood pressure Signal processing Feature extraction Physiology Quality assessment Biomedical monitoring Distribution functions Testing
Content Type	Text
Resource Type	Article

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