MIT-LCP / MIT-LCP/wfdb-python

qrs detector improvements

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enhancement help wanted
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Description

This will be the single thread for discussing improvements for qrs detectors. I will probably only be working on the xqrs class in the near future.

Current thoughts:

  • The filter used to accentuate the qrs (we'll call this the moving wave integration filter) is currently an upright ricker with a fixed width: wavelet_filter = signal.ricker(self.qrs_width, 4). There are many things we could improve upon, such as:
    • Inverting it if we figure out the r wave is inverted
    • Designing a set of template beats for different ecg channels.

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the xqrs class and inspect how wavelet_filter = signal.ricker(self.qrs_width, 4) is used in the QRS detector. The issue lists possible directions, including inversion for inverted R waves and templates for different ECG channels, but does not define one change or a completion test.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
bioinformatics
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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