Snore Timeline Zie je slaap als nooit tevoren

Waar worden mijn opnames opgeslagen?

You can export the entire night's audio as a single file, or bulk download all your snoring episodes at once. Exported files are saved to your iPhone's Files app.

To find your exported files:

1. Open the Files app on your iPhone
2. Tap Browse at the bottom of the screen
3. Under Locations, tap On My iPhone
4. Open the Snore Timeline folder

Your files are organized into:

Kan ik mijn opnames delen met anderen?

Yes. Recordings are saved to your Files app and can also be shared anywhere via the share sheet — AirDrop, Messages, email, ChatGPT, and more.

1. Navigate to the night you want to share
2. Tap the download icon at the top of the Snoring Episodes list
3. Select Export Full Night Audio — the file is saved to your Files app and a share sheet appears

1. Tap the download icon on any episode card — the file is saved to your Files app
2. Tap it again to open the share sheet and send it anywhere

Waar moet ik mijn telefoon plaatsen tijdens opname?

For snoring detection, 3-4 feet away works well. To capture breathing signals for disruption detection, place your phone 1-2 feet away—breathing must be clearly audible to be detected. Ideal placement is on a nightstand near your bed, plugged in for charging.

Wat maakt Snore Timeline anders dan andere snurkapps?

Snore Timeline focuses on what matters most: a complete, uncompromised view of your sleep. Unlike other apps that use data sampling, our snore recorder with timeline visualization captures and analyzes every sound in real-time. You get an interactive timeline that shows every snoring episode—no shortcuts taken, just meticulous attention to detail.

Heeft Snore Timeline een abonnement nodig?

No. Snore Timeline is completely free with no subscription required. All features are included: breathing disruption detection, episode grouping, nightly summaries, and data export—no hidden costs or in-app purchases.

Detecteert het ademstilstanden?

Yes, Snore Timeline detects breathing pauses by analyzing your sleep audio. It identifies extended silent periods (10+ seconds) followed by gasps or recovery sounds. The app marks these breathing disruptions on your timeline so you can see patterns. Note: This is audio analysis only, not a medical device. Breathing pauses may be worth discussing with a healthcare professional.

Hoe nauwkeurig is de snurkdetectie?

Snore Timeline uses Apple's proven sound classification technology to continuously analyze your audio in real-time throughout the night. Detection starts immediately with no calibration or setup needed. The app identifies snoring, gasps, coughs, breathing, and sleep talking by analyzing both the sound patterns and frequency characteristics of each audio segment. All processing happens entirely on your device for complete privacy.

Hoe toont de app episodes?

Snore Timeline is a snoring app that shows episodes by grouping consecutive snore signals together. When snoring is detected, the app creates an episode. If no snoring occurs for 30 seconds, that episode ends and a new one begins when snoring resumes. Each episode displays duration, snore count, and a plain-English summary. This helps you see patterns and understand exactly when you snored most during the night.

Zijn mijn gegevens privé?

Absolutely. All audio processing happens entirely on your iPhone—no cloud uploads, no external servers, no accounts required. Your recordings stay on your device and only you have access to them. You can export data to share with doctors while maintaining complete privacy.

Kan ik het gebruiken met AirPods of Bluetooth-apparaten?

Yes! Snore Timeline supports Bluetooth audio devices including AirPods, making it easy to record throughout the night without draining your phone's battery.

Hoeveel batterij gebruikt het?

Snore Timeline is optimized for overnight recording. Most users report 20-30% battery drain over 8 hours of recording. We recommend keeping your phone plugged in during recording for best results.

Why did the app stop recording before morning?

This usually happens when iOS interrupts the app's audio session while it runs in the background. Anything that plays a sound—a phone call, an alarm from another app, or certain notifications—can trigger an interruption. The app attempts to resume recording automatically, but if the interruption lasted too long, iOS will not allow it to restart without you reopening the app.

Fix: Enable Do Not Disturb or a Focus mode before you start recording. This blocks calls and notification sounds that would interrupt the session.

What dB threshold should I use for Loud Sound Detection?

Loud Sound Detection creates episodes whenever sounds exceed your chosen dB threshold. This is useful for catching sounds that are hard to classify—like whispers or mumblings that don't register as sleep talking. Snoring and sleep talking are already detected automatically without this setting. To find the right threshold: start a recording and stay quiet while watching the live dB meter. Note your room's ambient noise level (typically 30-40 dB), then set your threshold 15-20 dB higher so only meaningful sounds trigger episodes.

Which audio quality setting should I choose?

The audio quality settings exist to help you save storage space on your device. Ideally, you should always use High Fidelity for the best recordings. Only choose Basic (~8 MB/hour) or Standard (~15 MB/hour) if you need to save space—these trim higher frequencies to reduce file size. If you want to hear yourself breathing in recordings, you must use High Fidelity (~25 MB/hour), as Basic and Standard cut out the subtle frequencies where breathing sounds occur.

How does Snore Timeline estimate sleep stages?

Snore Timeline analyzes the regularity of your breathing throughout the night using your phone's microphone. After approximately 15 minutes of recording, the algorithm establishes your personal breathing baseline. It then classifies periods into Awake, Light Sleep, Deep Sleep, and REM based on research-backed thresholds—steady, regular breathing suggests deep sleep, while irregular patterns indicate REM. The algorithm is also time-aware, accounting for the natural progression of sleep stages throughout the night. These are estimates from audio analysis, not clinical measurements.

How accurate are the sleep stage estimates?

The sleep stage estimates are based on published research into the relationship between breathing regularity and sleep stages. They provide a useful picture of your general sleep architecture and trends over time. However, they have not been validated against polysomnography (clinical sleep studies) and should be treated as estimates, not diagnoses. For clinical sleep analysis, consult a healthcare professional.

Research using similar audio-based methods has achieved approximately 70% accuracy when validated against polysomnography.

Onderzoeksverwijzingen:

• Aging Effects on Cardiac and Respiratory Dynamics in Healthy Subjects across Sleep Stages (Schumann et al., 2010).Demonstrates that respiratory scaling exponents differ measurably between sleep stages. Deep sleep shows the most regular breathing patterns, while REM sleep produces more variable, irregular breathing. These distinctions inform the breathing regularity thresholds used to classify each stage.
• Using Spectral Continuity to Extract Breathing Rate from Heart Rate and Its Applications in Sleep Physiology (Orphanides et al., 2024).Confirms that deep sleep (N3) has the most stable and predictable breathing of any sleep stage. This finding is used to require consistently low breathing variance for deep sleep classification, and to exit deep sleep when variance increases.

Does sleep stage detection require any special setup?

No. Sleep stage estimation works automatically using the same audio recording as snoring and breathing disruption detection. Just place your phone 1-2 feet away on your nightstand and start recording. The algorithm needs approximately 15 minutes to establish your personal breathing baseline. For the most consistent results, use the same phone placement each night.