# Filter out linearly interpolated historical data points

I am reading in historical sensor data from a plant. I found out that there are intermittent periods where between time t1 and time t2, the data points are linearly interpolated. I came to know, that this is done by the server automatically, when the data is missing between t1 and t2 (eg. sensor being off etc.). I am providing an example below, where the data goes missing between A,B. Manually filtering out these unwanted data points improves my model quality by quite a lot. I would like to know what is a smart approach to filter out such data points apart from checking the slope ?

For example, a window of $$100$$ ms (? I can't see what your unit of measurement is in the question), appears to have quite a lot of variation going on in the non-interpolated sections, but is perfectly linear where interpolated, judging from the figure.