Train Test Split for overlapping samples
You can shuffle the samples first with a serial number attached to all the samples. Now, randomly select p% of the samples as test data and leave the (100-p)% samples as the training data. During this random selection, make sure that if a sample of serial #n has been selected as test, then #n-1 &amp; #n+1 are not picked.

In that way, 


You are not testing the classifier on a sequence of the data.
Avoid overlap between train &amp; test samples.