An adaptive real-time beat detection method for continuous pressure signals
Xiaochang Liu; Gaofeng Wang; Jia Liu
刊名Journal of Clinical Monitoring and Computing
2016
英文摘要A novel adaptive real-time beat detection method for pressure related signals is proposed by virtue of an enhanced mean shift (EMS) algorithm. This EMS method consists of three components: spectral estimates of the heart rate, enhanced mean shift algorithm and classi- fication logic. The Welch power spectral density method is employed to estimate the heart rate. An enhanced mean shift algorithm is then applied to improve the morphologic features of the blood pressure signals and detect the max- ima of the blood pressure signals effectively. Finally, according to estimated heart rate, the classification logic is established to detect the locations of misdetections and over detections within the accepted heart rate limits. The parameters of the algorithm are adaptively tuned for ensuring its robustness in various heart rate conditions. The performance of the EMS method is validated with expert annotations of two standard databases and a non-invasive dataset. The results from this method show that the sensi- tivity (Se) and positive predictivity (?P) are significantly improved (i.e., Se[99.45 %, ?P[98.28 %, and p value 0.0474) by comparison with the existing scheme from the previously published literature.
收录类别SCI
原文出处http://link.springer.com/article/10.1007/s10877-015-9770-z
语种英语
内容类型期刊论文
源URL[http://ir.siat.ac.cn:8080/handle/172644/10276]  
专题深圳先进技术研究院_数字所
作者单位Journal of Clinical Monitoring and Computing
推荐引用方式
GB/T 7714
Xiaochang Liu,Gaofeng Wang,Jia Liu. An adaptive real-time beat detection method for continuous pressure signals[J]. Journal of Clinical Monitoring and Computing,2016.
APA Xiaochang Liu,Gaofeng Wang,&Jia Liu.(2016).An adaptive real-time beat detection method for continuous pressure signals.Journal of Clinical Monitoring and Computing.
MLA Xiaochang Liu,et al."An adaptive real-time beat detection method for continuous pressure signals".Journal of Clinical Monitoring and Computing (2016).
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