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Sleep Stage Identification

Sleep Stage Identification. Jessie Y. Shen February 17, 2004. Objective. How Sleep Stage Identification fits into the Narcolepsy Project? Manual Sleep Staging Overview Review on Previous Automation Attempts Problems, Issues, and Solutions Work in Progress. Portable Device. Detection

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Sleep Stage Identification

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  1. Sleep Stage Identification Jessie Y. Shen February 17, 2004.

  2. Objective • How Sleep Stage Identification fits into the Narcolepsy Project? • Manual Sleep Staging Overview • Review on Previous Automation Attempts • Problems, Issues, and Solutions • Work in Progress

  3. Portable Device Detection Algorithm Current Condition Doctor’s New Instructions Expert System Estimated Future Condition GUI for Doctor Medication Allocation Prediction Algorithm Objective Evaluation of Patient Condition Suggested Actions Activity Planning GUI for Patient Stored Data Medication & Activity Narcolepsy Project Detection Algorithm

  4. Detection Algorithm • Goal: • Correctly identify the conscious level of subject while awake and the sleep stage while sleeping. • Method: • Quantify brain activity • Sleep staging automation Sleep staging automation

  5. Manual Sleep Staging • Standard set by Rechtschaffen and Kales • Awake, NREM I to IV, REM, MT • Polysomnogram: • EEG • EOG • EMG

  6. EEG

  7. Previous Research • Shimada 1998 – NN at 80% • 1st ANN for EEG to characteristic waves • 2nd ANN for characteristic waves to stage • 3rd ANN for contextual correction • Oropesa 1999 – Wavelet & NN at 77.6% • Flexer 2000 – HMM at 80%

  8. FYDP

  9. 5 Issues 1. Stages often changes during epoch. 2. Changes are gradual. 3. Some features are only present some of the time. 4. Sleep staging rules are not intuitive. 5. Medical experts have an inter-observer agreement of less than 90%.

  10. Solutions Mimic medical experts’ actions. 1. Extract Feature Information (Activity Band Info, Characteristic Wave Info, and Other Info) 2. Establish Contextual Information (last stage, the duration in the current stage, etc.) 3. Determine Sleep Stage by processing the feature and contextual information with a complete rule based expert system.

  11. Components

  12. Extract Feature Information • Mixed frequency activity • Spectrogram • Identify Awake and REM from other stages

  13. Extract Feature Information Awake REM sensitivity 93.51% specificity 94.60%

  14. Extract Feature Information III IV • Delta band content • Scalogram • Differentiate NREM II, III, and IV Stage II(90.23%, 86.06%), Stage III(98.60%, 96.81%), Stage IV(99.53%, 98.03%)

  15. Establish Contextual Information Standard Hypnogram For Healthy Young Adults

  16. Establish Contextual Information

  17. Establish Contextual Information Awake Stage I Stage II Stage III Stage IV REM

  18. Work in Progress • Extract Feature Information • Sleep spindles, K-complex, Saw-tooth waves, etc. • Establish Contextual Information • Consider duration of each stage, number of elapsed cycles, etc. • Build Rule-based Inference System

  19. Thank You!

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