Psychophysiology with Python
Project description
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)
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.
References
Peak detection (PPG signal)
Artefact detection and correction:
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.