# What function to apply to a signal to expose its burstiness (i.e., make the burst part more evident than the non-bursty part)

I am working on some signals one of which I have attached below:

What can I do (i.e. functions that I can apply to this signal) to get something like the signal below:

Note how one can draw a line at 0.25 to differentiate between rownumber <13000(or close), and rownumber>13000

-currently, I have tried checking how far each value of the signal is away from the mean, but that didn't seem to help either (i.e., the signal still did not look anywhere near my desired). By the way, I am "not" a signal processing guru, which means I probably would appreciate explanation intended for someone with little experience in signal processing.

Edit: Based on Dan's answer, I decided to clarify something on the question. I am using this signal for a machine learning related research, and the assumption is that I only have the sparse area, but this figure helps give insight into the nature of the dense part of the signal. Hence, in the above paragraph when I said I tried the deviation from the average, I mean as I received new signals (which looks like the dense part shown in the fig), I tried to find out how far it is from the mean of the sparse part of the signal. So I guess my new revised question will be- if you have the sparse part of the signal, and you want to use its nature, shape or amplitude to differentiate between the dense (which at the initial time is unknown) and the sparse signals, how would you do this?