The wavedat function uses the matlab algorithm wavedet, compiled for python.
The algorithm is described in the following paper: [1]. The function is calculating
the fiducial points of the ECG recording using wavelet transform.
Parameters:
matlab_pat – path to matlab runtime 2021a directory
peaks – Optional input- Annotation of the reference peak detector (Indices of the peaks), as an ndarray of shape (L,N), when L is the number of channels or leads and N is the number of peaks. If peaks are not given, the peaks are calculated with the jqrs detector.
Returns:
fiducials: Nested dictionary of leads - For every lead there is a dictionary that includes indexes for for each one of nine fiducials points.
The function is an Implementation of an energy based qrs detector [3]. The algorithm is an
adaptation of the popular Pan & Tompkins algorithm [2]. The function assumes
the input ecg is already pre-filtered i.e. bandpass filtered and that the
power-line interference was removed. Of note, NaN should be represented by the
value -32768 in the ecg (WFDB standard).
Parameters:
thr – threshold, default value is 0.8.
rp – refractory period (sec), default value is 0.25.
Returns:
indexes of the R-peaks in the ECG signal, as an ndarray of shape (L,N), when L is the number of channels or leads and N is the number of peaks.
The purpose of the Biomarkers class is to calculate the biomarkers, we divided the morphological biomarkers into two main groups: intervals and waves.
:param signal: The ECG signal as a ndarray.
:param fs: The sampling frequency of the signal [Hz].
:param fiducials: Nested dictionary of leads - For every lead there is a dictionary that includes indexes for for each one of nine fiducials points. this nested dictionary can be calculated using the FiducialPoints module.