We publish the numbers most sleep apps hide.

The "98% accurate" problem

Most consumer sleep apps say something like "98% accurate." I'll be honest — that number used to mean nothing to me, and I built one of these things.

"98% accurate" at what? Detecting that there was a sound? Distinguishing snores from background noise? Counting the right number of breathing pauses in a night? Different questions, different answers. Collapsing them into one rounded number is what you do when you don't want people to look closely.

The four numbers we actually have

These come from n=70+ paired nights — meaning we ran SomniSense on a phone next to the same person, on the same night, in a sleep lab where they were also being recorded with a real polysomnography setup. Audio was scored by AASM-trained sleep technicians who didn't know what SomniSense had said.

That last part — "didn't know what we said" — is what blinded scoring means. We don't get to pre-train our scorers on our own answers. Otherwise the test would be circular.

Why I'm publishing this before the paper

The honest reason: the academic peer review for the paper is in active preparation. It hasn't published yet. Once submitted, peer review typically takes another 3–6 months, plus the patent application timeline.

If I waited, you'd have nothing to compare other apps against in the meantime. So I'm publishing the numbers and the methodology now, with the explicit caveat: they're from our internal study and haven't been peer-reviewed yet. When the paper publishes, this page gets the citation. If peer review changes any number meaningfully, this page changes — and I'll explain what and why.

That's the deal. I'd rather tell you something that might be slightly off and let you push back than say nothing for six months.

What you should know about the methodology

Full whitepaper at /accuracy. The short version:

• Sample size: 70+ paired nights, adults with and without diagnosed sleep breathing issues. Demographic diversity documented in §6 of the whitepaper — including who's underrepresented (mostly: under-18, severe BMI extremes, certain ethnic groups). I want to be specific because "nights" without context can be misleading.
• Recording setup: a smartphone on the bedside table, 50–90 cm from the participant's head. iPhones and mid-range Androids from 2018 onward.
• Ground truth: in-lab polysomnography with synchronized audio, scored by AASM-trained sleep technicians blinded to what SomniSense said.
• Analysis: per-event sensitivity and precision; per-night Bland-Altman agreement of BRI vs AHI.

The algorithm builds on years of sleep apnea research. The current version was retrained from scratch and rebuilt for SomniAI LLC to handle smartphone audio specifically — different microphone, different distance, different acoustic environment than clinical hardware. Peer-reviewed publication and US patent application are in active preparation.

What this isn't

• Not a diagnostic claim. Even at these numbers, SomniSense isn't a medical device, doesn't diagnose sleep apnea, and isn't validated for users under 18.
• Not a personal guarantee. Your bedroom might be acoustically unusual. Your partner might snore louder than you. The model might catch fewer of your events. The methodology paper documents the conditions we tested under — read it if you want to know whether your scenario is in or out of distribution.
• Not a replacement for a sleep study. If your BRI runs above 15 consistently, that's a clinic conversation. We give you data to bring. The clinic gives you the diagnosis.