We have experimented with an Asa H in which we do not advance the time step and record input components until an input "changes significantly." (R. Jones, Trans. Kansas Academy Sci., vol. 117, pg 126, 2014) This can be done by storing and updating a running average of the input (a single component of the input vector OR the input similarity measure, a dot product for example) and a running average of the standard deviation (of the single component OR the similarity measure).
An average over time is involved so we can employ multiple copies of this algorithm, each looking over time windows (intervals) of different length.