I\'m trying to read a image from a electrocardiography and detect each one of the main waves in it (P wave, QRS complex and T wave). Now I can read the image and get a vector li
This is a wonderful question! I have a few thoughts:
Dynamic Time Warping could be an interesting tool here. You would establish "templates" for your three classes, and then using DTW could see the correlation between your template and "chunks" of the signal (break the signal up into, say, .5 second bits, ie. 0-.5 .1-.6 .2-.7...). I've worked with something similar for gait analysis with accelerometer data, it worked reasonably well.
Another option is a combined signal processing/ machine learning algorithm. Break your signal into "chunks" again. Make "templates" again (you'll want a dozen or so for each class) take the FFT of each chunk/template and then use a Naïve Bayes Classifier (or another ML classifier, but NB should cut it) to classify for each of your three classes. I've also tried this on gait data, and was able to get upwards of 98% precision and recall with relatively complicated signals. Let me know how this works, it's a very exciting problem.