I'm currently working with a database which contains several large PPG (pulse oximetry) and ECG time series. These series, however, contain segments within them which are highly contaminated by noise, making these segments differ heavily from "normal" segments of the time series.

So, my question is: is there some sort of algorithm which can return me a segmented time series in a way that the segments are either noise contaminated data or "normal" data?

Something that could return the indexes where the noise contaminated segments begin and end would be extremely helpful.

P.S.: I tried to remove the noise through highpass filtering and detrending, however I wasn't successful, and my objective is not simply filtering the data, but actually remove the noise contaminated segments.


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