The intensities are continuous. They have no upper bound. There are also no 0 intensity seizures. (I guess that would mean no seizure so its not included)
I imagine the stopping condition like a seq2seq decoder.

You feed the time series into the CNN. It produces the context/feature vector.
A decoder (LSTM) treats this like a NLP problem. e.g. Section 2.2 in Pointer-generator network 1 You have a  which doesn’t have any significance except to init the decoder. You then decode by timesteps, “event by event”, using the feature/context vector and the previous decoder input until you reach a stopping condition. I’m not sure of how the stopping condition is usually handled but I assume its similar in most seq2seq models. Instead of the decoder running a software over the probability distribution of a vocabulary, it would instead use a MSELoss criterion. This way it could produce N number of seizure events until it believes that it has output all the seizure events from the EEG data.

Again, not sure what, if any, of this is out of the box in PyTorch.