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PPG, IBI, HRV, and respiration workflow

Frozen R source: reference/vignettes/articles/ppg-hrv-workflow.Rmd

This page is the Python migration companion for the corresponding gpbiometrics 2.0.0 article. The original R article is retained verbatim in reference/vignettes/; this companion identifies the matching Python API so the scientific workflow can be reproduced without hiding the reference implementation.

Python API crosswalk

The frozen R article calls the following exported functions; all are available under the same names in gpbiometricspy:

  • gp.assess_gazepoint_sampling_irregularity(...)
  • gp.audit_gazepoint_ibi_quality(...)
  • gp.check_gazepoint_ppg_binary_quality(...)
  • gp.check_gazepoint_pyhrv_interval(...)
  • gp.compute_gazepoint_pyhrv_frequency_domain(...)
  • gp.compute_gazepoint_pyhrv_time_domain(...)
  • gp.correct_gazepoint_beats(...)
  • gp.correct_gazepoint_rri_artifacts_local(...)
  • gp.create_gazepoint_analysis_decision_log(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.create_gazepoint_reproducibility_statement(...)
  • gp.detect_active_biometric_channels(...)
  • gp.detect_gazepoint_biometric_schema(...)
  • gp.detect_gazepoint_biometric_timebase(...)
  • gp.detect_gazepoint_ppg_onsets(...)
  • gp.detect_gazepoint_ppg_peaks(...)
  • gp.estimate_gazepoint_breathing_rate_from_ibi(...)
  • gp.estimate_gazepoint_respiration_from_ppg(...)
  • gp.extract_gazepoint_hrv_features(...)
  • gp.extract_gazepoint_pyhrv_nn_intervals(...)
  • gp.filter_gazepoint_ibi_implausible(...)
  • gp.filter_gazepoint_ppg_signal(...)
  • gp.flag_gazepoint_ppg_quality(...)
  • gp.flag_gazepoint_rr_outliers(...)
  • gp.plot_gazepoint_ppg_breathing(...)
  • gp.plot_gazepoint_ppg_peak_detection(...)
  • gp.plot_gazepoint_ppg_segmentwise(...)
  • gp.prepare_gazepoint_heartpy_input(...)
  • gp.prepare_gazepoint_pyppg_input(...)
  • gp.prepare_gazepoint_rhrv_input(...)
  • gp.remove_gazepoint_ppg_baseline_wander(...)
  • gp.run_gazepoint_pyhrv_style(...)
  • gp.simulate_gazepoint_biometrics(...)
  • gp.standardise_gazepoint_biometric_names(...)
  • gp.summarise_gazepoint_hr_windows(...)
  • gp.summarise_gazepoint_hrv_features(...)
  • gp.summarise_gazepoint_ibi_hrv_windows(...)
  • gp.summarise_gazepoint_ibi_windows(...)
  • gp.summarize_gazepoint_beat_corrections(...)
import gpbiometricspy as gp

# Example entry point from this workflow
# result = gp.assess_gazepoint_sampling_irregularity(...)

Interpretation

Use the same conservative physiological interpretation as the R package: derived biometric features are signal-processing outputs and do not directly establish emotion, stress, cognition, preference, health status, or diagnosis.

Executable Python companion

The frozen R call crosswalk above is retained for completeness. The following companion is an executable end-to-end Python workflow using synthetic/public data and the same scientific domain. It is also executed by the test suite.

Run from the repository root:

python examples/tutorials/ppg-hrv-workflow.py

from __future__ import annotations
from _shared import *
d=pulse_frame(100,30); process=gp.process_gazepoint_ppg_heartpy_style(d,'pulse','time_s','participant',100,high_precision=False); nni=800+30*np.sin(np.linspace(0,12*np.pi,300)); pyhrv=gp.run_gazepoint_pyhrv_style(nni_ms=nni); freq=gp.compute_gazepoint_pyhrv_frequency_domain(nni,method='welch'); finish('ppg-hrv-workflow',process=process,pyhrv=pyhrv,frequency=freq)