systole 0.0.1

Psychophysiology with Python

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Systole is an open-source Python package providing simple tools to record and analyze body signal for psychophysiology.

Installation

Systole can be installed using pip:

pip install systole

The following package will be required to use Systole:

• Numpy (>=1.15)

• SciPy (>=1.3.0)

• Pandas (>=0.24)

• Matplotlib (>=3.0.2)

• Seaborn (>=0.9.0)

Recording

Systole supports the recording of PPG signal 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.

Record and plot data with 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

2 methods are available to record PPG signal:

• The read() method

Will continuously record for a certain amount of time (specified by the duration parameter, in seconds). This is the easiest and most robust method, but it is not possible to run instructions in the meantime (serial mode).

# Code 1 {}
oximeter.read(duration=10)
# Code 2 {}

• The readInWaiting() method

Will read all the available bytes (up to 10 seconds of recording). When inserted into a while loop, it allows recording 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.

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

Heart beat 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()

Artifact removal

It is possible to detect and correct outliers from RR time course following the method described in [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)

Heart rate variability

Time-domain

Extract the summary of time-domain indexes.

from systole.hrv import time_domain

stats = time_domain(rr)
stats

Frequency-domain

from systole.hrv import plot_psd

plot_psd(rr)

Extract the summary of frequency-domain indexes.

from systole.hrv import frequency_domain

frequency_domain(rr)

Output

Metric

Value

Non-linear

from systole.hrv import nonlinear

nonlinear(rr)

All the results have been tested against Kubios HVR 2.2 (<https://www.kubios.com>).

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.

Acknowledgement

This software 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 preexisting toolboxes dedicated to heart rate variability and signal analysis.

• HeartPy: https://python-heart-rate-analysis-toolkit.readthedocs.io/en/latest/

• hrv: https://github.com/rhenanbartels/hrv

• ECG-detector: https://github.com/berndporr/py-ecg-detectors

• Pingouin: https://pingouin-stats.org/

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