psgscoring

Open-source AASM-compliant respiratory scoring for polysomnography.

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Paper

Rombaut B, Rombaut B, Rombaut C, et al. Automated Polysomnography Scoring for Clinical Sleep Medicine: An Open-Source Platform Validated Against 59 Independent Scorer Sessions on PSG-IPA. Manuscript in preparation, 2026.

Technical details (signal processing chain, classification logic, all twelve bias corrections): Technical Reference (Online Supplement)

What this library does

psgscoring detects and classifies respiratory events (apneas, hypopneas, RERAs) in polysomnography recordings following AASM rules. It extends YASA (Vallat & Walker, eLife 2021) from sleep staging into a complete clinical respiratory scoring pipeline.

Three contributions that distinguish this library:

  1. Twelve bias corrections — the first systematic identification and empirical quantification of six over-counting and six under-counting mechanisms in automated respiratory scoring
  2. AHI confidence interval — every study is scored at three stringency levels (strict/standard/sensitive), yielding a per-study robustness grade (A/B/C) rather than a single AHI number
  3. Clinical auditability — every event carries a confidence score, classification rule index, and per-correction counters, enabling the reviewing physician to verify individual scoring decisions

Installation

pip install psgscoring

Requirements: Python ≥3.9, numpy, scipy, mne. No GPU required.

Quick Start

import mne
from psgscoring import run_pneumo_analysis

# Load EDF and provide a hypnogram (e.g., from YASA)
raw = mne.io.read_raw_edf("recording.edf", preload=True)
hypnogram = ["W", "N1", "N2", "N2", "N3", ...]  # per 30-s epoch

# Run the full pipeline
results = run_pneumo_analysis(raw, hypnogram, scoring_profile="aasm_v3_rec")

# Access results
resp = results["respiratory"]["summary"]
print(f"AHI: {resp['ahi_total']}, Severity: {resp['severity']}")
print(f"Events: {resp['n_obstructive']} OA, {resp['n_hypopnea']} Hyp")

# AHI confidence interval
interval = results["ahi_interval"]
print(f"AHI interval: [{interval['strict']['ahi']}–{interval['sensitive']['ahi']}]")
print(f"Robustness: {interval['robustness_grade']}")

Scoring Profiles

Parameter Strict Standard Sensitive
Hypopnea threshold ≥30% ≥30% ≥25%
SpO₂ nadir window 30 s 45 s 45 s
Peak-based detection No Yes Yes

Validation

PSG-IPA (PhysioNet): 5 recordings, 59 independent scorer sessions. Mean |ΔAHI| = 1.8/h, Pearson r = 0.997, severity concordance 4/5 (standard profile). See the paper for full results.

MESA (NSRR, external cohort): q=7 high-quality holdout, n=92 (held out from the optional LightGBM re-classifier's training). LightGBM-augmented AHI: bias −0.02/h, MAE 5.3/h, Pearson r = 0.87 against the NSRR nsrr_ahi_hp3u reference. SHHS-1 validation in progress.

Twelve Bias Corrections

# Correction Direction Clinical impact
1 Post-apnea baseline inflation Over-counting Prevents false Mild→Moderate
2 SpO₂ cross-contamination Over-counting Flags uncertain coupling
3 Cheyne-Stokes trough scoring Over-counting Prevents HF misdiagnosis as OSA
4 Low-confidence defaults Over-counting Confidence stratification
5 Artefact-flank exclusion Over-counting Prevents post-disconnect events
6 Local baseline validation Over-counting Rejects inflated-baseline FPs
7 Peak-based amplitude detection Under-counting AASM-conformant breath-level
8 Extended SpO₂ nadir window Under-counting Catches delayed desaturations
9 Flow smoothing removal Under-counting Eliminated +54 FPs on PSG-IPA
10 Position signal auto-mapping Under-counting Handles raw ADC encoding
11 Configurable profiles Under-counting Sensitivity adjustment per study
12 Flattening-based RERA Under-counting Flow limitation without amplitude drop

Architecture

~8,900 lines across 17 submodules, 115 unit tests (CI: Python 3.9–3.12):

constants · utils · signal · breath · classify · spo2 · plm · ancillary · respiratory · pipeline · ml_classifier · profiles · postprocess · signal_quality · signal_quality_channels · ecg_effort · _types

Related

  • YASAFlaskified — web platform integrating psgscoring with YASA staging, multilingual PDF reports, EDF+ export, and FHIR R4
  • YASA — AI-based sleep staging (Vallat & Walker, eLife 2021)
  • slaapkliniek.be — live instance (no installation required)

Citation

@article{rombaut2026psgscoring,
  title     = {Automated Polysomnography Scoring for Clinical Sleep Medicine:
               An Open-Source Platform Validated Against 59 Independent
               Scorer Sessions on {PSG-IPA}},
  author    = {Rombaut, Bart and Rombaut, Briek and Rombaut, Cedric},
  year      = {2026},
  note      = {Manuscript in preparation}
}

Disclaimer

psgscoring is research software — not a medical device. It is not CE-marked (MDR 2017/745) or FDA-cleared. All outputs are research-grade estimates that must be reviewed by a qualified clinician before any diagnostic or therapeutic decision. See DISCLAIMER.md for the full text.

License

BSD-3-Clause. See LICENSE.

Contact: bart.rombaut@gmail.com

Project links

Data verified by PyPI on Jul 26, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.32.0)

1 maintainer

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Avatar for bartromb from gravatar.com bartromb

Credits

Author/Maintainer: Briek Rombaut, Cedric Rombaut

GitHub Statistics

Data verified by PyPI on Jul 26, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.9

Provides Extra

full ml test plot

Tags

polysomnography PSG sleep AASM apnea hypopnea AHI respiratory scoring sleep-staging YASA hypoxic-burden

Classifiers

Report project as malware

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