Development center, Technology Development HQ, Omron Healthcare Co., Ltd, 53 Kunotsubo, Terado-cho, Muko, Kyoto, 617-0002, Japan.
Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, 3311-1, Yakushiji, Shimotsuke, Tochigi, 329-0948, Japan.
Med Biol Eng Comput. 2020 Jun;58(6):1393-1404. doi: 10.1007/s11517-020-02162-4. Epub 2020 Apr 13.
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.
血压变异性是心血管疾病（CVD）的重要危险因素之一。“血压激增”代表短期血压的剧烈波动，能够引发心血管事件。我们的新型血压监测设备可以有效评估血压激增。据我们所知，我们是第一个开发出自动检测连续“逐拍”（BbB）血压测量中血压激增的算法的团队。该算法可以帮助临床医生从患者的连续 BbB 血压测量大数据中节省大量时间识别血压激增。共有 94 名受试者（74 名男性和 20 名女性）参与了我们的研究来开发血压激增检测算法，总共从研究对象中收集了 3272 个血压激增。该血压激增检测算法是一种基于监督学习的简单分类模型，它将血压激增的形状制定为检测规则。使用 5 倍交叉验证，根据专家手动标记的血压激增对我们算法识别的血压激增进行评估。该算法的召回率和精确率分别为 0.90 和 0.64。每个受试者的处理时间为 11.0±4.7 秒。我们的算法足以用于临床实践，并将有助于更好地了解 CVD 发病的这一独特方面。
