How does the Apple Watch Series 10 differentiate between REM and deep sleep

Sleep Stage Classification

1. Sleep Stages Identified:
- The Apple Watch categorizes sleep into three main stages:
- REM Sleep: Associated with dreaming and brain activity.
- Core Sleep: Often referred to as light sleep, which includes stages one and two of non-REM sleep.
- Deep Sleep: The restorative phase of sleep.

2. Data Collection Method:
- The watch employs an accelerometer to detect movement and a heart rate sensor to monitor changes in heart rate variability (HRV) during sleep. This data is analyzed using algorithms that estimate the time spent in each sleep stage based on patterns observed during the night[4][3].

Differentiation Between REM and Deep Sleep

1. Motion Analysis:
- The Apple Watch tracks subtle movements, including those caused by breathing, which helps it determine when a user is in REM versus deep sleep. During REM sleep, there may be more irregular movements compared to the more stable state of deep sleep[3].

2. Heart Rate Monitoring:
- Changes in heart rate provide additional insights; for instance, heart rates tend to fluctuate more during REM sleep due to increased brain activity. In contrast, deep sleep is characterized by a lower and more stable heart rate[4][2].

Accuracy and Limitations

1. Accuracy Rates:
- Studies indicate that the Apple Watch achieves about 85% accuracy in detecting deep sleep but has lower accuracy (around 75%) for REM sleep. This discrepancy can lead to significant variations in reported sleep stages compared to clinical assessments using polysomnography (PSG) [2][5].

2. Variability Among Users:
- Individual factors such as movement patterns, physiological differences, and potential sleep disorders can affect the accuracy of the watch's readings. Some users report discrepancies between the Apple Watch's data and other devices or apps, highlighting that while it provides useful trends, it may not always reflect precise values [1][6].

In summary, while the Apple Watch Series 10 employs advanced sensors and algorithms to estimate REM and deep sleep effectively, its accuracy can vary significantly among users, making it a useful tool for tracking general sleep patterns rather than providing definitive measurements.