QRS Detection:
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 Butterworth_filterfor noise removal and QRS detection via wavelet transform. The .get_qrs() method returns a dataframe including a column labeling each detected QRS timepoint.
