How your breathing sounds become stage estimates, and how to read them.
Snore Timeline estimates which sleep stage you were in at each point of the night: Awake, Light Sleep, Deep Sleep, or REM, plus a fifth label called Silence for stretches it could hear nothing at all. The estimates come from your breathing sounds alone, captured by the microphone you already use for snore detection. This page explains the signals behind each stage, how to read the hypnogram in your nightly summary, and what the estimates can and cannot tell you.
Your breathing changes character as you move through the stages of sleep, and those changes are audible. The app analyzes the regularity and variance of your breathing patterns captured through the microphone, the same recording that powers snore detection and breathing disruption detection, so stage estimation needs no extra setup.
After approximately 15 minutes of recording, the algorithm establishes your personal breathing baseline: your typical rate and regularity. It then measures the rest of the night against your own baseline rather than population averages, applying research-based thresholds to classify each period into a stage.
Breathing regularity carries most of the signal, but the algorithm also weighs:
The result appears as colored bands behind your timeline and as a hypnogram chart in your nightly summary.
Each stage leaves a distinct fingerprint in your breathing:
Because Deep Sleep sits at one end of the regularity scale and REM at the other, the app requires consistently low breathing variance before it classifies a period as Deep Sleep, and it exits Deep Sleep when variance rises. The sound classification running alongside supplies snore and movement context.
Audio-based staging has an honest limitation: it can only classify what it can hear. When your breathing is too soft for the microphone to resolve into a stage, yet nothing suggests you are awake, the app marks that stretch as Silence instead of guessing. This happens often for quiet breathers, or when a fan or air conditioner masks the sound.
Silence is treated as restful sleep. It counts toward your total sleep time, and it earns credit toward the restorative portion of your sleep score, so a genuinely quiet night still reads as good sleep. Inventing a stage from no evidence would make the data less trustworthy, so the app labels the uncertainty instead.
Two breathing measurements feed the stage model, and both appear in your results:
Both are audio-based estimates for personal insight. A respiratory rate well outside the normal range is worth discussing with a doctor rather than self-diagnosing.
Yes, when it is available. If a wearable such as Apple Watch reports sleep stage data for the night, Snore Timeline uses the watch's Deep and REM percentages for your sleep score instead of its own audio-derived estimates. A wearable measures those stages more accurately than audio analysis can, so the app defers to it. Without a connected wearable, the app falls back to its breathing- and movement-based estimates. The Apple Watch page covers what the watch contributes.
The hypnogram in your nightly summary charts your progression through the stages across the night. Each colored band represents 5 minutes in a particular stage:
Read it left to right and you can count how many complete sleep cycles you went through, see when you got the most Deep Sleep, and spot when REM occurred. Stage bands also appear behind the waveform on your timeline, which lets you check whether snoring episodes line up with a particular stage.
The nightly summary also shows the percentage of time you spent in each stage. A typical night might look like:
These percentages vary with age, health, stress levels, and other factors, so compare your nights against each other rather than against a textbook. For scoring, the app uses Deep Sleep of roughly 13 to 23 percent as a healthy target, with 20 percent or more earning credit toward your score, and a REM target of about 20 to 25 percent. Hitting these ranges can add points to your sleep score. They are general guidelines, not a medical assessment.
The classification rests on published research into the relationship between breathing regularity and sleep stages. Research that stages sleep from a single physiological signal, such as breathing or heart-rate variability, reaches roughly 70% agreement with polysomnography, the clinical sleep study standard. The estimates give you a useful picture of your general sleep architecture and, more valuably, your trends over time.
Several things move accuracy up or down. Longer recordings help, because several hours of sleep lets the app observe more complete sleep cycles. Noisy environments, a bed partner, pets, or sleeping far from the microphone all reduce how reliably your breathing is detected, and the estimates degrade with it.
These are estimates from audio analysis, not clinical measurements. Clinical sleep staging uses brain waves, eye movement, and muscle activity, signals that audio cannot capture. Snore Timeline has not been validated against polysomnography and cannot diagnose sleep apnea or any sleep disorder. Treat the stage breakdown as a general guide, and talk to a healthcare professional if you have concerns about your sleep.
Stage estimation requires audible breathing throughout the recording. If a night shows no stages, one of these is the usual cause:
Use the same phone placement every night, quiet the room where you can, and record full nights. Recordings of 6 or more hours give the algorithm the most complete cycles to work with, and consistent placement keeps your nights comparable.
If the whole timeline came up empty, not just the stages, see Troubleshooting.
The sleep stage classification algorithm is informed by:
