I am fitting a logistic regression model using 4 indepdendent features. These features are counts of signal features present in a time series dataset. For example, number of peaks, number of troughs, etc. We have approximately 100,000 samples, however these have been measured from signals of differing length (between 10 and 60 minutes). The most 'truly' representative data are from those samples acquired for 60 minutes, however many of the samples are from observations less than 20 minutes. Converting these signal feature counts to the frequency domain (e.g. 10 peaks per hour) results in an overestimation of certain features from the shorter signal duration samples because we know, for example, that 100 peaks per hour does not occur. The frequency of features within these signals are non-steady and change significantly throughout recording. Only once a recording duration of >40 minutes are the signal features most reliable.
Therefore, the question is: is there a way to 'penalise' the datapoints that are from shorter signal lengths (e.g. <30 minutes) in the model training process?