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%DET in EMG signals

Posted: Fri Jun 21, 2019 03:20
by jaimehislop
I have been looking at recurrence and determinism as indicators of fatigue in EMG signals, and would like to apply this process to my data. I have been trying to replicate the process done in a similar study (Keshavarz Panahi, A., & Cho, S. (2016). Prediction of Muscle Fatigue during Minimally Invasive Surgery Using Recurrence Quantification Analysis. Minimally invasive surgery, 2016, 5624630. doi:10.1155/2016/5624630). However it seems a very intensive process in terms of time and my computer processing capabilities (which I do understand as it is comparing every point to every other point). My sample rate was 2000Hz (approximately 10x higher than the study I am referencing) and using crqa (is that the right function to use?) can only take a few seconds of data at a time. I was just wondering if there were any recommendations in terms of downsampling, minimum window lengths (either in time or number of data points), or processing methods to try and optimize this process.
Thanks, from Jaime