Multiscale entropy analysis of biological signals
Multiscale entropy analysis of biological signals
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DOI：
10.1103/physreve.71.021906
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发表时间：
2005-02-01
影响因子：
2.4
通讯作者：
Peng, CK
中科院分区：
文献类型：
--
作者：
Costa, M;Goldberger, AL;Peng, CK
传统的测量生物信号复杂性的方法不能考虑这种时间序列固有的多个时间尺度。当将这些算法应用于在健康和疾病状态下获得的真实世界数据集时，这些算法产生了相互矛盾的结果。详细描述了多尺度熵(MSE)方法的基本原理和实现方法。我们扩展和阐述了以前的发现，表明它适用于生理和病理条件下人类心跳的波动。该方法始终表明，随着年龄的增长，复杂性降低，出现反复无常的心律失常(房颤)，以及危及生命的综合征(充血性心力衰竭)。此外，这些不同的情况有不同的MSE曲线曲线，提示诊断用途。研究结果支持了衰老和疾病的一般“复杂性损失”理论。我们还将该方法应用于编码和非编码DNA序列的分析，发现后者具有更高的多尺度熵，这与所谓的垃圾DNA序列包含重要生物信息的新观点是一致的。
Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct MSE curve profiles, suggesting diagnostic uses. The results support a general "complexity-loss" theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information.