psgscoring 0.7.2

Open-source Python library for AASM 2.6-compliant automated polysomnography scoring

psgscoring

Open-source AASM 2.6–compliant respiratory scoring for polysomnography.

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 2.6 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.

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