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+1 vote
in Machine Learning by (19k points)

I'm trying to read an image from 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 like (4.2; 4.4; 4.9; 4.7; ...) representative of the values in the electrocardiography, which is half of the problem. I need an algorithm that can walk through this vector and detect when each of these waves starts and ends.

Here is an example of one of its graphs:

alt text

It would be easy if they always had the same size, but it's not like it works, or if I knew how many waves the ECG would have, but it can vary too. Does anyone have some ideas?


1 Answer

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by (33.1k points)
edited by

Your problem looks similar to onset detection, but the problem context is not similar to that. This type of biological signal processing, i.e., detection of the P, QRS, and T phases, that can exploit knowledge of specific time-domain characteristics of every waveforms. 

One approach that works for QRS detection is dynamic time warping. The time-domain characteristics remain invariant, Then it would work remarkably well.

A wavelet-based approach seems most intuitive to me. The wavelets are suited here is that they are useful at parameterizing a wide variety of shapes regardless of time or amplitude scaling. 

Hope this answer helps.

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