Power analysis of low frequency component

AI Thread Summary
To measure the power of a low-frequency event at ~0.2Hz in a voltage-time signal, the window size for power spectral analysis should ideally be at least 1/0.2Hz, which translates to a minimum of 5 seconds. However, since the event is short (~30ms) and occurs every 5-10 seconds, achieving a balance between window size and the number of segments is crucial to minimize noise while retaining data. Averaging the events and calculating the power spectral density (PSD) of the average or taking the PSD of individual segments and averaging those results are both viable approaches. The periodic nature of the discharging activity raises questions about the utility of power spectral analysis, as it may not behave like traditional oscillatory signals. Ultimately, careful consideration of window size and analysis method is essential for accurate power measurement.
Dan Kanak
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Hello,
If I wish to measure the power of a very low-frequency periodic event (~.2Hz) in a voltage-time signal what is an ideal window size to use for power spectral analysis. The activity is very short in duration (~30ms) and occurs every 5-10 seconds. My problem is that I need a relatively large number of segments so that I can discard artifactual noise without losing data, but it seems to me that the window size is limited to at least 1/.2Hz. Is this true, or should the window length be at least 30ms. Since the activity isn't really oscillatory but rather periodic discharging, is power spectral analysis even useful in this case? Thanks for your time.

Dan
 
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You have a choice. Average the events and take the PSD of the average. Take the PSD of each and average the answers. At least I think so; I'm rusty at PSD analysis.
 

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