An outlier detection for this data
Not sure if this is time series data, but it looks like it might be.

For any given narrow window of time there is a distribution that looks like it's centered somewhere a bit higher than y=200 and has a reasonably stable spread. So whatever that distribution is, that's your model.

Then monitor the recent window, and treat that as a sample. Perform a goodness of fit test against the model. If it fails then the points in the window are anomalous.

UPDATE: Based on what you say about the data representing roughly a minute of time, it looks like you could use a window that's about 1 second wide. Run a hypothesis test to see if the data in the window comes from the typical distribution, and if not, just mark all the points in that window as being anomalous. If you need finer resolution than 1s, simply shrink the window.