ApneaSenseby SomniAI LLC

Research & validation

The methodology, the numbers, and the code — checkable.

These are SomniAI LLC's internal validation results, validated against polysomnography. Three papers, three open-source repositories. Don't take the numbers on faith — run the code on your own data.

The validation set

Detection was evaluated against in-lab and ambulatory polysomnography (PSG): 80 paired PSG nights across 40 participants — 10 in-lab PSG and 70 ambulatory PSG nights with a nasal-airflow cannula, smartphone audio captured simultaneously. The ambulatory majority means most of the data is real home environments, not a quiet lab.

Two time-aligned tracks — acoustic detection above, PSG reference below — with most events lining up between them.
Illustrative A schematic of how acoustic detection is scored against the PSG reference, event by event — not plotted data. The actual agreement is the accuracy numbers above and the per-seed results in the papers and code below.

The progression — baseline → attention → production

One connected line of work: each builds on the one before — a cascade, its attention-based Stage-2 classifier, then that classifier compressed for the device. All three papers and their code are here.

Step 1 · baseline & cascade

Two CNN baselines + the cascaded two-stage pipeline

The baseline snore and apnea CNNs, with multi-seed bootstrap validation — and the Stage-1→Stage-2 cascade that the next two build on.

94.29%snore accuracy (93.60–95.02)
83.82%apnea baseline accuracy
0.983snore AUC-ROC
Read the paper (PDF) → Run the code (GitHub) → Cite — Zenodo DOI →
Step 2 · attention (Stage-2)

Coordinate-Attention 1D — the Stage-2 classifier

Replaces the baseline apnea CNN above as the cascade’s Stage-2 classifier: a 14,001-parameter attention model that keeps temporal position — a 93.2% parameter cut, with accuracy preserved or improved.

87.14%accuracy (85.14–89.68)
14,001parameters
93.2%param reduction vs baseline
Read the paper (PDF) → Run the code (GitHub) → Cite — Zenodo DOI →
Step 3 · compression → production

Compression → the on-device production model

Compresses the Stage-2 model above to production size: INT8 quantization-aware training + 50% structured pruning + CoreML. On this dataset, compression raises test accuracy.

88.49%accuracy (INT8 + pruned)
56.4 KB9,416 INT8 params
0.064 msinference · Apple Neural Engine
Read the paper (PDF) → Run the code (GitHub) → Cite — Zenodo DOI →

What we publish, and what the patent covers

The papers and code disclose the full method — the cascaded two-stage architecture over a compact 200×3 @ 1 Hz representation, the Coordinate-Attention 1D formulation, the quantization and pruning protocol, and the evaluation methodology — so the results are reproducible. Certain production specifics — multi-stage gating, event-driven triggering, and the privacy-preserving on-device system architecture — are covered by three co-filed U.S. provisional patent applications (SomniAI LLC) and are not described.

Limitations we hold to

  • Acoustic proxy. We detect sound, not airflow or blood oxygen — PSG and continuous SpO₂ measure the physiological event more directly. We don't claim to beat them.
  • Screening, not diagnosis. ApneaSense is not a medical device and is not FDA-cleared.
  • Precision-first. Tuned to avoid false positives, so it under-counts — read the rate as a conservative lower bound.
  • Sample. Recruited with sleep-breathing complaints; generalization across BMI and healthy cohorts is ongoing.

Citing this work

Authored at SomniAI LLC by the inventor on the pending U.S. patent (PAT-001). Each paper is published on Zenodo with a citable DOI — linked under each paper above. ORCID: 0009-0002-4798-5161. An arXiv mirror is planned.

Want to evaluate it on your own data? See how the SDK works or email the founder.