用于信号分析的速度矩。
On moment of velocity for signal analysis.
作者信息
Dorraki M, Fouladzadeh A, Allison A, Davis B R, Abbott D
机构信息
School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia.
Centre for Biomedical Electrical Engineering (CBME), The University of Adelaide, Adelaide, South Australia 5005, Australia.
出版信息
R Soc Open Sci. 2019 Mar 27;6(3):182001. doi: 10.1098/rsos.182001. eCollection 2019 Mar.
The instantaneous frequency (IF) of a signal is a well-defined quantity that is widely used for analysing non-stationary signals. However, often in practice, IF as a function of time can possess large spikes and negative values. Moreover, IF is very sensitive to noise, limiting its range of practical application. Due to these deficiencies, we introduce the concept of moment of velocity (MoV) for signal analysis. As a case study, we compare the performance of MoV to a standard Hilbert transform-based approach for R-wave identification in human electrocardiogram signals, demonstrating that our approach is more robust to noise. We examine characteristic heartbeats obtained from the MIT-BIH Arrhythmia database. A detection error rate of 0.07%, a positive predictive value of 99.97%, and a sensitivity of 99.95% are achieved against analysis results from the database.
信号的瞬时频率（IF）是一个定义明确的量，广泛用于分析非平稳信号。然而，在实际应用中，IF作为时间的函数常常会出现大幅尖峰和负值。此外，IF对噪声非常敏感，限制了其实际应用范围。由于这些不足，我们引入了速度矩（MoV）的概念用于信号分析。作为一个案例研究，我们将MoV的性能与基于标准希尔伯特变换的方法在人体心电图信号的R波识别方面进行了比较，结果表明我们的方法对噪声更具鲁棒性。我们检查了从麻省理工学院 - 贝勒医学院心律失常数据库中获取的特征心跳。与数据库的分析结果相比，实现了0.07%的检测错误率、99.97%的阳性预测值和99.95%的灵敏度。
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