I have a numpy array of shape N_Samples x 360, as an example, consider the following:
[ 89 94 56 - - - 55] <-360 length vector [ 56 98 55 - - - 98] | | | | [ 67 55 99 - - - 77] Total N such vectors
There is one label 0 or 1 corresponding to each of the 360- length vector I am asked to train this using CNN but everywhere I am observing that CNN is used for image classification. I am confused about the following:
- Is CNN appropriate for such data?
- If yes, then how concepts like max pooling and strides are applicable?
- Should we use Conv1D instead of Conv2D?
I will also appreciate if some helpful papers or blogs are provided with respect to this context on how to adjust shapes of input and weight.
Edit: The 360-length vector is showing ECG signal values of one heartbeat. Keeping R-peak in the middle, I have 180 signal values after and 180 before the R-Peak to have 360 long chunks represent heartbeats (Sampling Rate: 360). I am using it to classify each heartbeat as arrhythmic or non-arrhythmic.