Staniczenko Phillip P A, Lee Chiu Fan, Jones Nick S
Physics Department, Clarendon Laboratory, CABDyN Complexity Centre, Oxford University, Oxford OX1 1HP, United Kingdom.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jan;79(1 Pt 1):011915. doi: 10.1103/PhysRevE.79.011915. Epub 2009 Jan 21.
We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the power spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s, it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s, this becomes 89.5%, and with 60 s, it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.
心房颤动和心房扑动。这种频谱熵测量方法的依据是三种心律期间心跳时间功率谱的特征差异。我们将从十个心跳窗口得出的患者数据绘制在“紊乱图”上，并确定频谱熵值水平和方差中定义心律的范围。在自动心律失常检测算法中使用频谱熵能够从患者心跳时间序列中对心房颤动时期进行分类。当该算法设置为在6秒内识别异常心律时，它与专业心律评估人员标注的85.7%一致；对于30秒的响应时间，这一比例变为89.5%，60秒时则为90.3%。该算法提供了一种快速检测心房颤动的方法，显示出低至6秒的可用响应时间。频域中的紊乱测量在一系列生物信号中具有实际意义：本文所述技术在快速识别其他节律信号中的紊乱方面具有潜在应用价值。
