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	An Analytical Model for Regular Respiratory Signals Derived from the Probability Density Function of Rayleigh Distribution
	Xin Li; Ye Li
	2015
会议名称	IEEE EMBC 2015
会议地点	Milano
英文摘要	Regular respiratory signals (RRSs) acquired with physiological sensing systems (e.g., the life-detection radar system) can be used to locate survivors trapped in debris in disaster rescue, or predict the breathing motion to allow beam delivery under free breathing conditions in external beam radiotherapy. Among the existing analytical models for RRSs, the harmonic-based random model (HRM) is shown to be the most accurate, which, however, is found to be subject to consi- derable error if the RRS has a slowly descending end-of-exhale (EOE) phase. The defect of the HRM motivates us to construct a more accurate analytical model for the RRS. In this paper, we derive a new analytical RRS model from the probability density function of Rayleigh distribution. We evaluate the derived RRS model by using it to fit a real-life RRS in the sense of least squares, and the evaluation result shows that, our presented model exhibits lower error and fits the slowly descending EOE phases of the real-life RRS better than the HRM.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/7280]
专题	深圳先进技术研究院_医工所
作者单位	2015
推荐引用方式 GB/T 7714	Xin Li,Ye Li. An Analytical Model for Regular Respiratory Signals Derived from the Probability Density Function of Rayleigh Distribution[C]. 见:IEEE EMBC 2015. Milano.

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