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Alternative TitleAtrial Fibrillation Pulse Detection via Complex Network Method
李晗; 赵海; 陆育卉; 邵士亮
Source Publication计算机科学
Volume44Issue:6Pages:237-239, 273
Indexed ByCSCD
Contribution Rank2
Funding Organization国家自然科学基金资助项目(61101121)资助
Keyword脉搏波 阵发性房颤 复杂网络 可视图法 支持向量机
Other AbstractIn order to explore the complexity of pulse wave, combined with the concept of "atrial fibrillation pulse" in traditional Chinese medicine, a complex network method to detect atrial fibrillation was presented. The photoplethysmograph pulse wave is thereby transformed to a network topology using visibility graph method. A binary classification support vector machine (SVM) based on Gausssian kernel function is designed to distinguish between normal sinus rhythm and atrial fibrillation. The degree distribution of the network and the average heart rate are extracted as the input features of the SVM. According to the experimental results of patients with paroxysmal atrial fibrillation, this method can effectively identify the patient's disease status and normal status.
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Document Type期刊论文
Corresponding Author李晗
Recommended Citation
GB/T 7714
李晗,赵海,陆育卉,等. 基于复杂网络映射的房颤脉检测[J]. 计算机科学,2017,44(6):237-239, 273.
APA 李晗,赵海,陆育卉,&邵士亮.(2017).基于复杂网络映射的房颤脉检测.计算机科学,44(6),237-239, 273.
MLA 李晗,et al."基于复杂网络映射的房颤脉检测".计算机科学 44.6(2017):237-239, 273.
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