Key result
An automatic detection algorithm identified surge blood pressure from continuous beat-by-beat measurements with a recall of 0.90 and precision of 0.64 compared to expert manual labeling.
Why the study?
Surge BP is a pathological exaggerated BP increase capable of triggering cardiovascular events, but an automatic algorithm to identify it efficiently from continuous beat-by-beat measurements was lacking.
Does an automatic detection algorithm accurately identify surge blood pressure from continuous beat-by-beat measurements in patients with sleep disorders or nocturnal hypertension compared to expert manual labeling?
Observational (n=94)
No
Does an automatic detection algorithm accurately identify surge blood pressure from continuous beat-by-beat measurements in patients with sleep disorders or nocturnal hypertension compared to expert manual labeling?
A newly developed automatic detection algorithm can identify surge blood pressure from continuous beat-by-beat measurements with high recall (0.90), enabling efficient analysis of large datasets in clinical practice.
Enables surge BP research in CVD; hypothesis-generating and requires validation before practice change.
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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Kokubo et al. (2020) conducted an observational in Sleep disorder or nocturnal hypertension (n=94). Automatic surge BP detection algorithm vs. Expert manual labeling was evaluated on Recall of surge BP detection. An automatic detection algorithm identified surge blood pressure from continuous beat-by-beat measurements with a recall of 0.90 and precision of 0.64 compared to expert manual labeling.
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