pecg.ecg package

pecg.ecg.FiducialPoints

class pecg.ecg.FiducialPoints.FiducialPoints(signal: array, fs: int)

Bases: object

The purpose of the FiducialPoints class is to calculate the fiducial points.

Parameters:

• signal – the ECG signal as a ndarray, with shape (L, N) when L is the number of channels or leads and N is the number of samples.

• fs – The sampling frequency of the signal.[Hz]

from pecg.ecg import FiducialPoints as Fp
fp = Fp.FiducialPoints(f_ecg_rec, fs)

wavedet(matlab_pat: str, peaks: array = array([], dtype=float64))

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.

[1]

Martinze at el (2004), A wavelet-based ECG delineator: evaluation on standard databases. IEEE Transactions on Biomedical Engineering, 51(4), 570-581.

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.

matlab_pat = '/usr/local/MATLAB/R2021a'
peaks = fp.jqrs()
fiducials = fp.wavedet(matlab_pat, peaks)

epltd()

This function calculates the indexes of the R-peaks with epltd peak detector algorithm. This algorithm were introduced by [2].

[2] (1,2)

Pan, Jiapu, and Willis J. Tompkins. “A real-time QRS detection algorithm.” IEEE Trans. Biomed. Eng 32.3 (1985): 230-236.

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.

peaks = fp.epltd()

xqrs()

This function wraps the XQRS function of the WFDB package.

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.

peaks = fp.xqrs()

jqrs(thr: float = 0.8, rp: float = 0.25)

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).

[3]

Behar, Joachim, Alistair Johnson, Gari D. Clifford, and Julien Oster. “A comparison of single channel fetal ECG extraction methods.” Annals of biomedical engineering 42, no. 6 (2014): 1340-1353.

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.

peaks = fp.jqrs()

pecg.ecg.Biomarkers

class pecg.ecg.Biomarkers.Biomarkers(signal: array, fs: int, fiducials: dict)

Bases: object

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.

from pecg.ecg import Biomarkers as Obm
obm = Obm.Biomarkers(f_ecg_rec, fs, fiducials)
ints, stat_i = obm.intervals()
waves, stat_w = obm.waves()

intervals()

Returns:

• intervals_b: Dictionary that includes all the row data, for the Interval duration and segments biomarkers.

• intervals_statistics: Dictionary that includes the mean, median, min, max, iqr and std, for every Interval duration and segments biomarker.

Interval duration and segments:

Biomarker	Description
P-waveint	Time interval between P-on and P-off.
PRint	Time interval between the P-on to the QRS-on.
PRseg	Time interval between the P-off to the QRS-on.
PRint2	Time interval between P-peak and R-peak as defined by Mao et al.
QRSint	Time interval between the QRS-on to the QRS-off.
QTint	Time interval between the QRS-on to the T-off.
QTcBint	Corrected QT interval (QTc) using Bazett’s formula.
QTcFriint	QTc using the Fridericia formula.
QTcFraint	QTc using the Framingham formula.
QTcHint	QTc using the Hodges formula.
T-waveint	Time interval between T-on and T-off.
TPseg	Time interval between T-off and P-on.
RRint	Time interval between sequential R-peaks.
Rdep	Time interval betweem Q-on and R-peak.

waves()

Returns:

• waves_b: Dictionary that includes all the row data, for every Waves characteristic biomarker.

• waves_statistics: Dictionary that includes the mean, median, min, max, iqr and std, for every Waves characteristic biomarker.

Waves characteristics:

Biomarker	Description
P-wave	Amplitude difference between P-peak and P-off.
T-wave	Amplitude difference between T-peak on and T-off.
R-wave:	R-peak amplitude.
P-waveArea	P-wave interval area defined as integral from the P-on to the P-off.
T-waveArea	T-wave interval area defined as integral from the T-on to the T-off.
QRSArea	QRS interval area defined as integral from the QRS-on to the QRS-off.
STseg	Amplitude difference between QRS-off and T-on.
J-point	Amplitude in 40ms after QRS-off as defined by Hollander et al.

Module contents