medhacks-zzg

MedHacks 2018 Team ZZG

Pitch

Target Problem: How can we understand the quality of our sleep and regularly adjust our sleep habits without requiring professional help?

Meet ZZG, your personalized, at-home sleep lab. ZZG provides tailored sleep hygiene suggestions based on Emotiv EPOC + EEG data.

Features

  • EEG data defines quantitative metrics such as stress levels and excitement during sleep
  • EEG data can be used to predict sleep disorders by identifying time spent in the 5 sleep stages
  • Assess quality of sleep based on entropy metric of sleep waves
  • ZZG uses EEG data to provide recommendations to improve quality of sleep (mocked)

Future work:

  • Use EEG to determine sleep quality using entropy index
    • Sleep quality entropy calculation: http://daedalus.scl.sztaki.hu/phdws2004/abstract/phdws2004_abstract_4.pdf
    • Sleep labs calculate sleep quality through sleep efficiency index, (Hours in stage 3 + Hours in stage 4)/(total hours of sleep). This is inconvenient because you need to go to a sleep lab to do this.
    • Paper suggests that there is a correlation between sleep efficiency index and proposed entropy index. More positive entropy, poorer quality of sleep. Above -1.313 means poor sleep. Need at least 2 seconds intervals of EEG data to calculate this.
  • Connect to visualization API to visualize EEG data in real time
  • Lifestyle questionnaire
    • Collect initial data on lifestyle to help provide recommendations
    • How would we use this data?
  • Community health functionality
    • Status quo data: Map that shows that sleep quality is lacking geographically
    • Demonstrates need for ZZG
    • App user geographical data: Show how user’s data compares to users around them
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