Sleep stagging Classification

[mahek0620]

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Feature Description

Implement a feature to classify different sleep stages (NREM and REM sleep) based on polysomnographic data, including EEG, EOG, and EMG signals. This feature would analyze these signals to accurately determine the sleep stages and patterns.

Use Case

A user wears a compatible sleep monitoring device while they sleep. The device records polysomnographic data, which is then analyzed by the AI assistant, Kai, to classify sleep stages. The user receives a detailed report on their sleep patterns, including time spent in each sleep stage, sleep efficiency, and any detected sleep disturbances.

Benefits

Improved Sleep Quality: Provides users with detailed insights into their sleep patterns, helping them understand and improve their sleep quality. Personalized Recommendations: Offers tailored advice on improving sleep hygiene based on detected sleep patterns and disturbances. Health Monitoring: Helps in early detection of sleep disorders, enabling timely medical intervention. Enhanced User Engagement: Encourages users to engage with the app by providing valuable and personalized sleep data. Data-Driven Insights: Collects data that can be used for further research and development in the field of sleep medicine and mental health.

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Priority

High

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[mahek0620]

@TAHIR0110 please assign this issue to me

[TAHIR0110]

@mahek0620 assigned!