Wavelet analysis of circadian and ultradian behavioral rhythms.
Wavelet analysis of circadian and ultradian behavioral rhythms.
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DOI：
10.1186/1740-3391-11-5
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发表时间：
2013-07-01
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通讯作者：
Leise TL
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作者：
Leise TL
我们回顾时间频率的方法，可以是有用的量化昼夜节律和超昼夜模式的行为记录。这些记录通常显示可能无法通过常用的措施（如活动开始）捕获的细节，因此可能需要替代方法。例如，活动可能涉及一天内持续时间和强度不同的多次发作，或者可能表现出周期和超日活动模式的日常变化。离散傅立叶变换和其他类型的周期图可以估计昼夜节律的周期，但我们表明，他们可能无法正确评估超昼夜周期。此外，这些方法无法检测周期随时间的变化。时频分析方法可以将频率估计在时间上局部化，更适合于分析超日周期和周期波动。连续小波变换提供了一种用于确定瞬时频率的方法，该方法在时间和频率上具有良好的分辨率，能够检测在几天的过程中的昼夜节律周期的变化以及在给定的一天内的超昼夜周期的变化。离散小波变换将时间序列分解成与不同频带相关联的分量，从而便于去除噪声和趋势或隔离感兴趣的特定频带。为了演示基于小波的分析，我们将变换应用到数值生成的示例和各种仓鼠行为记录。如果使用得当，小波变换可以揭示使用其他常用分析方法难以提取的模式，但必须谨慎应用和解释。
We review time-frequency methods that can be useful in quantifying circadian and ultradian patterns in behavioral records. These records typically exhibit details that may not be captured through commonly used measures such as activity onset and so may require alternative approaches. For instance, activity may involve multiple bouts that vary in duration and magnitude within a day, or may exhibit day-to-day changes in period and in ultradian activity patterns. The discrete Fourier transform and other types of periodograms can estimate the period of a circadian rhythm, but we show that they can fail to correctly assess ultradian periods. In addition, such methods cannot detect changes in the period over time. Time-frequency methods that can localize frequency estimates in time are more appropriate for analysis of ultradian periods and of fluctuations in the period. The continuous wavelet transform offers a method for determining instantaneous frequency with good resolution in both time and frequency, capable of detecting changes in circadian period over the course of several days and in ultradian period within a given day. The discrete wavelet transform decomposes a time series into components associated with distinct frequency bands, thereby facilitating the removal of noise and trend or the isolation of a particular frequency band of interest. To demonstrate the wavelet-based analysis, we apply the transforms to a numerically-generated example and also to a variety of hamster behavioral records. When used appropriately, wavelet transforms can reveal patterns that are not easily extracted using other methods of analysis in common use, but they must be applied and interpreted with care.