What I noticed from the literature is that for automated heartbeat classification ECG segmentation is an essential pre-processing step.
However, I wonder that they are fixing the same beat length for all types of heartbeats such as normal,PVC, APC, paced beat etc. In practical situations, how far it is advisable?
Next, from paper to paper segmentation lengths are different then how can they compare their results with others?
If it is desirable to fix the length of a segment why can't we choose a window that covers P, QRS, T events rather using fiducial points?
For segmentation of ECG , for various heartbeats as you are looking for one must use adaptive filter / wavelet filters or you have to use segmentation algorithm which is trained for detecting variation by itself based on neural network appoach
Hi Nima,
I agree with you. But, if we consider various types of beats it is not possible to fix the same window length. However, for classification problem why researchers are fixing same segment length.
Hello. I am new to ECG signal processing, but not to signal processing in general. As far as I can see, an ECG is essentially a high SNR, variable period signal. You therefore have what is a time-varying frequency tracking problem, similar to sonar signal processing but with a much better SNR. The suggestion of using a short-time spectrum or wavelet-based estimator seems reasonable. A spectrogram or time-frequency map should give an easily tracked signal that can be segmented based on its features. There are many approaches to doing this type of tracking, but a simple normalisation stage with threshold might work if the SNR is high and reliable.
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