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. Graphical abstract Surge blood pressure (surge BP) which is defined as pathological short-term (several tens of seconds) exaggerated BP increase capable of triggering cardiovascular events. We have already developed a wearable continuous beat-by-beat (bBb) BP monitoring device and observed surge BPs successfully in obstructive sleep apnea patients. In this, we developed an algorithm for the automatic detection of surge BP from continuous BbB BP measurements to save significant time identifying surge BP among > 30,000 BbB BP measurements. Our result shows this algorithm can correctly detect surge BPs with a recall of over 0.9.
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http://dx.doi.org/10.1007/s11517-020-02162-4 | DOI Listing |
Biomed Opt Express
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Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
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Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
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View Article and Find Full Text PDFEnviron Sci Technol
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School of Basic Medical Sciences, Shandong Second Medical University, 7166 Baotong West Street, Weicheng District, Weifang, Shandong 261053, China.
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View Article and Find Full Text PDFJ Tradit Complement Med
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National Research Institute of Chinese Medicine, Ministry of Health and Welfare, Taipei City, 112026, Taiwan.
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