快速检测节律性生物信号中的紊乱：识别心律失常的谱熵测量
Rapidly detecting disorder in rhythmic biological signals: A spectral entropy measure to identify cardiac arrhythmias
摘要：
我们考虑使用功率谱紊乱的连续测量来区分心脏的正常窦性心律和两种形式的心律失常：心房颤动和心房扑动。该谱熵测量是由三种节律期间的搏动定时的谱中的特征差异激发的。我们绘制患者数据来自十拍窗口上的“障碍地图”，并确定节奏定义范围的频谱熵值的水平和方差。在自动心律失常检测算法内采用频谱熵使得能够从患者搏动的时间序列中对心房颤动的周期进行分类。当算法被设置为在6 s内识别异常节律时，它与专业节律评估者的注释的85.7%一致;对于30 s的响应时间，这变为89.5%，对于60 s，这是90.3%。该算法提供了一种快速检测房颤的方法，表明可用的响应时间低至6秒。在频域中的紊乱的措施有实际意义的范围内的生物信号：本文所述的技术具有潜在的应用，在其他节奏信号的快速识别紊乱。
英文摘要：
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 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 6 s. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
