| > The lack of data for cat seizures is a challenge in this endeavour Not so much as you might think. The approach I'd take (from airborne geophysics) is to treat the datasets as "environmental normal" and pull out tens of thousand of (overlapping) 5 minute data runs and treat those as input vectors to an SVD (Singular Value Decomposition) reduction which becomes the kernal of monitoring going forward. Next rig up the cat in question with accelorometers and record data just as was done in the prior datasets. Your input now is a continuous pipeline (say every 20secs) of "the last five minutes of data" as a vector - reduce each vector to kernal (spanned by the basis for the "normal" dataset) + noise (doesn't match the normal span). There will be a regular amout of "noise"; seizures and unusual behaviour should spike the amount of noise and deserve attention. After a bit, you'll know what you're looking for (/cough /handwave /details). This, more or less, is how "out of band" signal is found in 256 channel radiometric spectrometer surveys - primed with a back catalog of hours of regular boring survey data and trained to look for the abnormal. |