How do I train a deep learning algorithm to find the template for an image and then compare other images to this template?

Hi, I am using MATLAB R2020a on a MacOS. I am currently trying to detect abnormal phase-space trajectories in the 2D space on a cycle-by-cycle basis as part of an ECG signal. Is there any way of training an algorithm with 'normal' trajectories within that signal and then using this ground truth as a template against which to compare the trajectory associated with each new cycle to find and flag abnormal cycles? Within the signal, there would be mostly normal cycles with intermittent periods of abnormality.

Below is an example of what would constitute a 'normal' trajectory:

This is an example of an abnormal cycle, but abnormal trajectories have variable morphologies:

Please note though, that the comparison would not be against a fixed 'normal' template, but rather a normalised template for a particular individual's signal.

Any suggestions would be very much appreciated. Thanks in advance

Answer 1

[Nikhil Sonavane]

You may refer to the following documentation link for more details on it-

• Train Neural Network for Deep Learning

[Cai Chin]

Hi, thanks for your answer. I trained a LSTM neural network to classify time-dependent 2-dimensional input signals derived from ECG's. I have used this example to train the network on raw data first but this took about an hour with a 77% classification accuracy. The example suggests using time-frequency analysis for feature extraction to speed up the training process and improve accuracy but this involves altering the method for one-dimensional data:

Is there any way of doing this feature extraction using 2-dimensional input signals. If not this method of feature extraction, is there another method for feature extraction that might be more suitable?