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Systole is an open-source Python package providing simple tools to record and analyze, cardiac signals for psychophysiology. In particular, the package provides tools to pre-process, analyze, and synchronize cardiac data from psychophysiology research. This includes tools for data epoching, heart-rate variability, and synchronizing stimulus presentation with different cardiac phases via psychopy.

The documentation can be found under the following link.

Installation

Systole can be installed using pip:

pip install systole

The following packages are required to use Systole:

  • Numpy (>=1.15)

  • SciPy (>=1.3.0)

  • Pandas (>=0.24)

  • Matplotlib (>=3.0.2)

  • Seaborn (>=0.9.0)

Recording

Systole natively supports the recording of PPG signals through the Nonin 3012LP Xpod USB pulse oximeter together with the Nonin 8000SM ‘soft-clip’ fingertip sensors. It can easily interface with PsychoPy to record PPG signal during psychological experiments, and to synchronize stimulus deliver to e.g., systole or diastole.

For example, you can record and plot data in less than 6 lines of code:

import serial
from systole.recording import Oximeter
ser = serial.Serial('COM4')  # Add your USB port here

# Open serial port, initialize and plot recording for Oximeter
oxi = Oximeter(serial=ser).setup().read(duration=10)

Interfacing with PsychoPy

The Oximeter class can be used together with a stimulus presentation software to record cardiac activity during psychological experiments.

  • The read() method

will record for a predefined amount of time (specified by the duration parameter, in seconds). This ‘serial mode’ is the easiest and most robust method, but it does not allow the execution of other instructions in the meantime.

# Code 1 {}
oximeter.read(duration=10)
# Code 2 {}
  • The readInWaiting() method

will only read the bytes temporally stored in the USB buffer. For the Nonin device, this represents up to 10 seconds of recording (this procedure should be executed at least one time every 10 seconds for a continuous recording). When inserted into a while loop, it can record PPG signal in parallel with other commands.

import time
tstart = time.time()
while time.time() - tstart < 10:
    oximeter.readInWaiting()
    # Insert code here {...}

Online detection

Online heart beat detection, for cardiac-stimulus synchrony:

import serial
import time
from systole.recording import Oximeter

# Open serial port
ser = serial.Serial('COM4')  # Change this value according to your setup

# Create an Oxymeter instance and initialize recording
oxi = Oximeter(serial=ser, sfreq=75, add_channels=4).setup()

# Online peak detection for 10 seconds
tstart = time.time()
while time.time() - tstart < 10:
    while oxi.serial.inWaiting() >= 5:
        paquet = list(oxi.serial.read(5))
        oxi.add_paquet(paquet[2])  # Add new data point
        if oxi.peaks[-1] == 1:
          print('Heartbeat detected')

Peaks detection

Heartbeats can be detected in the PPG signal either online or offline.

Methods from clipping correction and peak detection algorithm is adapted from 1.

# Plot data
oxi.plot_oximeter()
https://github.com/embodied-computation-group/systole/raw/master/Images/recording.png

Artefact removal

Systole implements the artefact rejection method recently proposed by Lipponen & Tarvainen (2019) 2.

from systole import import_rr()
from systole.plotting import plot_subspaces

rr = import_rr().rr[:100]
rr[20] = 1600  # Add missed beat

plot_subspaces(rr)
https://github.com/embodied-computation-group/systole/raw/master/Images/subspaces.png

Heartrate variability

Systole supports basic time-domain, frequency-domain and non-linear extraction indices.

All time-domain and non-linear indices have been tested against Kubios HVR 2.2 (<https://www.kubios.com>). The frequency-domain indices can slightly differ. We recommend to always check your results against another software.

from systole.hrv import plot_psd

plot_psd(rr)
https://github.com/embodied-computation-group/systole/raw/master/Images/psd.png

Development

This module was created and is maintained by Nicolas Legrand and Micah Allen (ECG group, https://the-ecg.org/). If you want to contribute, feel free to contact one of the contributors, open an issue or submit a pull request.

This program is provided with NO WARRANTY OF ANY KIND.

Acknowledgements

This software and the ECG are supported by a Lundbeckfonden Fellowship (R272-2017-4345), and the AIAS-COFUND II fellowship programme that is supported by the Marie Skłodowska-Curie actions under the European Union’s Horizon 2020 (Grant agreement no 754513), and the Aarhus University Research Foundation.

Systole was largely inspired by pre-existing toolboxes dedicated to heartrate variability and signal analysis.

References

Peak detection (PPG signal)

1

van Gent, P., Farah, H., van Nes, N., & van Arem, B. (2019). HeartPy: A novel heart rate algorithm for the analysis of noisy signals. Transportation Research Part F: Traffic Psychology and Behaviour, 66, 368–378. https://doi.org/10.1016/j.trf.2019.09.015

Artefact detection and correction:

2

Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time series artefact correction using novel beat classification. Journal of Medical Engineering & Technology, 43(3), 173–181. https://doi.org/10.1080/03091902.2019.1640306