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Can your mattress
score every breathing pause?

A thin strip under the mattress scores breathing events overnight — then builds an AHI — without wires on the body.

AHI visual — mattress signal traces used for overnight breathing-event scoring

Overnight mattress signal — the continuous trace the model reads to find breathing pauses and build an AHI, without wires on the body.

r = 0.94
Correlation, model AHI vs. PSG AHI (n=61)
0.8 /hr
Mean bias — no significant over/under-call
7.3 /hr
Mean absolute error — lowest of published systems reporting MAE
1.00
AUC for detecting severe OSA (AHI ≥ 30/hr)

From mattress signal to research report

A single strip on the mattress reads body motion and breathing as one continuous signal. A deep-learning model turns that overnight trace into second-by-second labels for breathing events, arousals, and sleep — then builds an AHI from those events.

Mattress signal
Signal features
Deep model
Events + sleep
AHI

Which events are easiest to catch?

At the individual-event level, distinct signal patterns are easier to separate than subtle ones. Central apnoea — a flat, unmistakable drop in effort — is well recognised; hypopnoea, a smaller partial reduction, is the hardest to pin down and is most often mistaken for normal breathing.

Central apnoea64% correct
Mixed apnoea56% correct
Hypopnoea52% correct
Obstructive apnoea78% correct*
Correctly classified → Mixed apnoea → Central apnoea → Normal breathing
“Even when the event type is mis-labelled, the model still finds it — temporal overlap with expert scoring runs 0.70–0.75 median IoU across all four respiratory classes, meaning the timing of the disturbance is rarely missed.” — According to Lead Algorithm Engineer Cham Nguyen

Epoch-level discrimination (AUC)

0.99
Central
0.93
Obstructive
0.97
Mixed
0.87
Hypopnoea

Severity agreement

Confusion matrix of PSG-diagnosed vs. model-predicted OSA severity category, independent test set (n=29 shown).

NormalMildMod.Severe
Normal 2 4
Mild 1 3 1
Mod. 3 2 2
Severe 11
Rows = PSG diagnosis, columns = model prediction. Every severe case (11/11) was correctly flagged; misclassifications cluster one band away, mostly at the normal/mild border.

How it ranks

Against published contact-free / under-mattress OSA detectors:

SystemRMAE
ZG-S01A ultrawideband radar0.97
Sonomat piezo mattress0.89
This study — mattress piezo0.947.3/hr
PVDF film mattress array0.94
Withings under-mattress9.5/hr
SleepMinder radio biomotion0.90
MAE reported by only 2 of 12 comparator studies — this system's 7.3 events/h was the lowest of the two.

Why it matters

Aggregate AHI can hide event-level detail. Scoring individual apnoea and hypopnoea events — not just a nightly count — preserves more of the overnight pattern for research review.

Known weak spot

Hypopnoea remains genuinely ambiguous from mattress motion alone — 39% read as normal breathing — reflecting the subtlety of the underlying signal, not a modelling shortcut.

What's next

Multi-site, home-based validation; adding acoustic sensing; multi-scorer PSG consensus to sharpen the hardest classes.

Quick answers

What does this system actually score?

Individual breathing events overnight — apnoeas, hypopnoeas, and related disturbances — then summarises them as an AHI compared with expert-scored PSG.

Which events are hardest?

Hypopnoeas. They are partial reductions and look more like normal breathing from mattress motion alone. Central apnoeas — a clearer flat drop — are easier to catch.

Is this a clinical product yet?

These results are research validation against concurrent PSG. They support further study work; they are not a cleared medical device claim for diagnosis or treatment decisions.