How to do early classification of time series event with small dataset? I would like to build a real-time binary classifier that can predict an event of interest that is occurring as soon as it starts. These are electromyographic signals, and the event classification should be able to classify the event as early as possible. This is because the next stage of the algorithm has to make a decision before the end of the event.
I don't know what kind of algorithm/approach I should use here. I suppose RNN with LSTM cells should do the job, but the dataset is quite small as physiological signals are not easy to gather.
I have seen many algorithms that windowed the signals (from the training set) and labeled each window as an event of interest if at least part of the event is contained in the window. Each window is then fed to a machine learning algorithm. Then, the prediction uses a sliding window in real-time. But this approach doesn't take into account the temporal aspect of the event as there is no link between each window seen by the ML algorithm.
Do you have any tips or resources I could use to solve the problem?
One kind of system you could look into are Echo State Networks (ESNs).
They are relatively cheap to train and can learn to predict output signals to an  arbitrary degree of precision.
All you need to train the system is some labeled training data.
Thus, if you have a sequence of measurements/feature values and the corresponding sequence of class labels, you can train and fine-tune these kinds of systems to output some required class label very quickly after the onset of an event encoded in the measured feature values.