Implements an ECG beat (acutally R-peak/QRS-complex) detection algorithm based on the article An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator with some tweaks.
NumPy and SciPy and matplotlib if you want to use the plotting.
The module can be run from command line. Reads newline-delimited raw ECG sample values and takes the sampling rate in Hz as an argument. In default mode outputs the detected peak sample numbers in the same format. Eg:
python2 rpeakdetect.py 128 < ecg_data.csv > beat_samples.csv
With added argument plot plots the detection.
python2 rpeakdetect.py 128 < ecg_data.csv
Running the latter with some sample data produces something like the image below. (The recording is from mobile setting with a rather unconventional electrode placment, hence the noisiness and a bit weird ECG waveform.)
Also note that despite the name, the algorithm doesn't actually detect the R-peaks themselves. Rather the detected time is better described as "midpoint of the QRS complex". Further the implementation may cause an artificial shifting of a few (1-2) samples due to not compensating the signal shifting during taking differences. If you need/want to detect the exact R-peak, it's quite straightforward to find by locating the maximum signal value in a small (some milliseconds) window around the detected position.
