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. Narcolepsy Project Portable Device Detection Algorithm Prediction Algorithm Expert System Medication Allocation Activity Planning GUI for Patient GUI for Doctor Stored Data Objective Evaluation of Patient Condition Doctor’s New Instructions Current Condition Medication & Activity Estimated Future Condition Suggested Actions 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% –1 st ANN for EEG to characteristic waves –2 nd ANN for characteristic waves to stage –3 rd ANN for contextual correction Oropesa 1999 – Wavelet & NN at 77.6% Flexer 2000 – HMM at 80%

8. FYDP Approach 1Approach 2 MethodMLPHMM FeaturesFrequencyHjorth OutputAwake/Asleep Awake, NREM I to IV, REM Accuracy91.81%77.36% Time Delay0.4 min3.5 min False Positive10.03%10.87%

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 Delta band content Scalogram Differentiate NREM II, III, and IV IIIIV 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. 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!