Tutorials

For an introduction to Systole and cardiac signal analysis, you can refer to the following tutorial:

Signal extraction and interactive plotting

The package integrates a set of functions for interactive or non interactive data visualization based on Matplotlib and Bokeh.

from systole.plots import plot_raw

plot_raw(signal[60000 : 120000], modality="ecg", backend="bokeh",
            show_heart_rate=True, show_artefacts=True, figsize=300)

Artefacts detection and rejection

Artefacts can be detected and corrected in the RR interval time series or the peaks vector using the method proposed by Lipponen & Tarvainen (2019).

from systole.detection import ecg_peaks
from systole.plots import plot_subspaces

# R peaks detection
signal, peaks = ecg_peaks(signal, method='pan-tompkins', sfreq=1000)

plot_subspaces(peaks, input_type="peaks", backend="bokeh")

Heart rate variability analysis

Systole implements time-domain, frequency-domain and non-linear HRV indices, as well as tools for evoked heart rate analysis.

from bokeh.layouts import row
from systole.plots import plot_frequency, plot_poincare

row(
    plot_frequency(peaks, input_type="peaks", backend="bokeh", figsize=(300, 200)),
    plot_poincare(peaks, input_type="peaks", backend="bokeh", figsize=(200, 200)),
    )

Online systolic peak detection, cardiac-stimulus synchrony, and cardiac circular analysis

The package natively supports recording of physiological signals from the following setups: - Nonin 3012LP Xpod USB pulse oximeter together with the Nonin 8000SM ‘soft-clip’ fingertip sensors (USB). - Remote Data Access (RDA) via BrainVision Recorder together with Brain product ExG amplifier (Ethernet).

Interactive visualization of BIDS structured datasets

from systole.viewer import Viewer

view = Viewer(
    input_folder="/BIDS/folder/path/",
    pattern="task-mytask",
    modality="beh",
    signal_type="ECG"
)

Inserting and removing peaks

Annotating bad segments