Tools for processing raw electrocardiogram data
The QRS complex is the most prominent feature in ECG signal and is therefore useful for quantifying timing of individual heartbeats. This allows for quantifying useful physiological metrics such as R-R interval, heart rate, heart rate variability as well as providing timing for measurement of other cardiovascular signals such as beat-to-beat systolic/diastolic blood pressure, brain blood flow velocity or muscle sympathetic nerve activity.
The ecg_wavelet() class in ecg_processing.py provides a butter lowpass filter for noise removal and QRS detection via wavelet transform. The .get_qrs() method returns a dataframe including a column labeling each detected QRS timepoint.
