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

list_profiles() enumerates the full registry (AASM v1/v2/v3, CMS/Medicare, Chicago 1999, and the NSRR dataset profile).

aasm_v3_breath — breath-graded hypopnea scoring (v0.13.0, opt-in)

run_pneumo_analysis(raw, hypnogram, scoring_profile="aasm_v3_breath")

A second hypopnea detector that takes the breath, not the sample, as the unit. The AASM speaks of peak signal excursions; this scores them directly. Five differences from the default detector:

  1. Breath-level events. An event is a run of consecutive breaths below the threshold, so boundaries land on breath transitions and a recovery breath ends the run by itself — no smoothing, and the merge/split problem that depresses event-F1 largely disappears.

  2. Two-pass patient calibration. Pass 1 finds the incontestable events; pass 2 measures the baseline from only the breathing that is not an event, and derives that patient's own SpO₂ delay by cross-correlation instead of assuming a fixed 30–45 s window.

  3. Graded AASM predicates. The rule structure stays literally the Rule 1A conjunction — ≥30% reduction AND ≥10 s AND (≥3% desaturation OR arousal) — but each threshold gets a tolerance rather than being infinitely sharp.

  4. Every event carries p_scored plus a criteria dict giving each predicate's contribution, so the audit trail says why, not just what.

    p_scored ranks events by how well they satisfy the AASM conjunction. It is not the probability that a scorer would mark the event: measured against the 12-scorer fractions on PSG-IPA (163 events) the correlation is only r = 0.194 and the level is +0.33 too high. Use it to order events, not as a likelihood.

  5. One strictness axis (hypopnea_strictness, default 0.50) instead of three parameter combinations.

Scope is hypopneas only; apneas keep the existing detector.

Status — opt-in, not the default. On PSG-IPA (5 recordings, 12 scorers) the median event-F1 rises 0.343 → 0.434, the percentile within the inter-scorer distribution p6 → p17, and mean |ΔAHI| against the scorer median falls 1.84 → 0.29. On MESA the paired advantage replicates on two disjoint held-out samples of 50 (p = 0.0069 and p = 0.0016). But the two datasets disagree on the absolute level by ~16 AHI points, and until that is explained the existing clinical output stays the default. See docs/interim_conclusie_klinisch_gebruik.md.

aasm_v3_dual — apneas on both flow sensors (v0.14.0, opt-in)

run_pneumo_analysis(raw, hypnogram, scoring_profile="aasm_v3_dual")

The AASM assigns apneas to the oronasal thermistor and hypopneas to nasal pressure. Choosing one sensor means inheriting its blind spot: nasal pressure misses mouth breathing, the thermistor is too slow for short events. This profile detects apneas on both and merges them, so neither channel can veto the other.

Each merged apnea carries corroboration — both, thermistor_only or pressure_only. Nothing is discarded on that basis. Across 1785 apnea detections on MESA only 19% are seen by both sensors, so requiring corroboration would remove four events in five. Discarding can be licensed explicitly (corroborate_apnea_events(..., corroboration_licensed=True)); no profile does so.

meta["flow_channels"]["thermistor_check"] reports the envelope correlation between the two channels either way. Under this profile it is informational — the second sensor is additive and can only add events. It gates only the substitutive path, where a dead thermistor would delete them.

Why this exists. v0.13.2 made the thermistor the apnea sensor outright. On a montage where that channel carried no usable breathing signal, apneas went from 93 to 0 and the conclusion moved from moderate CSAS — human-confirmed — to mild SAS. That release was rolled back. aasm_v3_rec remains the default and is byte-identical.

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

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Credits

Author/Maintainer: Briek Rombaut, Cedric Rombaut

GitHub Statistics

Data verified by PyPI on Aug 3, 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

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