Paper Info
Title
Automatic detection algorithm for establishing standard to identify "surge blood pressure".
Abstract
Blood pressure (BP) variability is one of the important risk factors of cardiovascular disease (CVD). “Surge BP,” which represents short-term BP variability, is defined as pathological exaggerated BP increase capable of triggering cardiovascular events. Surge BP is effectively evaluated by our new BP monitoring device. To the best of our knowledge, we are the first to develop an algorithm for the automatic detection of surge BP from continuous “beat-by-beat” (BbB) BP measurements. It enables clinicians to save significant time identifying surge BP in big data from their patients’ continuous BbB BP measurements. A total of 94 subjects (74 males and 20 females) participated in our study to develop the surge BP detection algorithm, resulting in a total of 3272 surges collected from the study subjects. The surge BP detection algorithm is a simple classification model based on supervised learning which formulates shape of surge BP as detection rules. Surge BP identified with our algorithm was evaluated against surge BP manually labeled by experts with 5-fold cross validation. The recall and precision of the algorithm were 0.90 and 0.64, respectively. Processing time on each subject was 11.0 ± 4.7 s. Our algorithm is adequate for use in clinical practice and will be helpful in efforts to better understand this unique aspect of the onset of CVD.
Year
DOI
Venue
2020
10.1007/s11517-020-02162-4
Medical & Biological Engineering & Computing
Keywords
DocType
Volume
Clinical informatics, Medical informatics applications, Expert systems, Learning from labeled data, Blood pressure monitors
Journal
58
Issue
ISSN
Citations 
6
0140-0118
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Ayako Kokubo100.34
Mitsuo Kuwabara200.34
Hiroshi Nakajima300.34
Naoko Tomitani400.34
Shingo Yamashita501.35
Toshikazu Shiga6276.43
Kazuomi Kario710.68