Pan-Tompkings In Code

Recap

We saw a basic implementation of the Pan-Tompkins algorithm and visualized how the pre-processing steps change the waveform. On noisy signals, with small QRS complexes, we saw just how well the pre-processing steps could improve our signal. Still, we saw that sometimes it would not be enough and more complex detection rules need to be implemented.

In the following exercise, we implement more sophisticated detection rules for just this purpose.

The overall goal here isn’t necessarily to understand the Pan-Tompkins algorithm per se; it’s really to get to a place where we can solve this kind of problem in any domain where you have some knowledge of the underlying physiology and the signal characteristics and the noise characteristics and you can do for your task what Pan-Tompkins algorithm does for QRS complex detection. As you learn more about these tools and operations that can boost signal and suppress noise in your specific domain, you’ll be able to build a better intuition for designing your own algorithm on your own problem.

Notebook Review

If you wanted to interact with the notebook in the video, you can access it here in the repo /ecg-processing/walkthroughs/QRS-complex-detection/ or in the workspace below.

Exercise 1: Extend Pan-Tompkins Algorithm

Instructions

1. Complete the Offline or Online instructions below.
2. Read through the whole .ipynb.
3. Complete all the code cells that contain ## Your Code Goes Here.

Offline

1. In the repo which you can access here in the repo /ecg-processing/exercises/1-pan-tompkins-algorithm/) you should find the following files:

• 1_pan_tompkins_algorithm.ipynb
• A00010.npz
• A01593.npz

1. Open up the python notebook and associated files in your desired editor.

Note: Instructions can be found in Introduction to Wearable Data's Concept Developer Workflow for how to set up your local environment.

Online

1. Go to the next concept and the 1_pan_tompkins_algorithm.ipynb should be open and the workspace should already contain the appropriate data files, A00010.npz and A01593.npz.