sleep stage identification jessie y. shen february 17, 2004
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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
Narcolepsy ProjectPortable Device
DetectionAlgorithm
PredictionAlgorithm
Expert System
Medication Allocation
Activity Planning
GUI forPatient
GUI forDoctor
StoredData
ObjectiveEvaluation of Patient Condition
Doctor’s New Instructions
Current Condition
Medication & Activity
EstimatedFuture Condition
Suggested Actions
DetectionAlgorithm
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
Manual Sleep Staging
• Standard set by Rechtschaffen and Kales
• Awake, NREM I to IV, REM, MT• Polysomnogram:
– EEG – EOG – EMG
EEG
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%
FYDPApproach 1 Approach 2
Method MLP HMM
Features Frequency Hjorth
Output Awake/Asleep Awake, NREM I to IV, REM
Accuracy 91.81% 77.36%
Time Delay 0.4 min 3.5 min
False Positive 10.03% 10.87%
5 Issues1. 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%.
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.
Components
Extract Feature Information• Mixed frequency activity• Spectrogram• Identify Awake and REM from other stages
Extract Feature InformationAwake REM
sensitivity 93.51% specificity 94.60%
Extract Feature Information
• Delta band content• Scalogram• Differentiate NREM
II, III, and IV
III IV
Stage II(90.23%, 86.06%), Stage III(98.60%, 96.81%),Stage IV(99.53%, 98.03%)
Establish Contextual InformationStandard Hypnogram
For Healthy Young Adults
Establish Contextual Information
States Awake NREM I NREM II NREM III NREM IV REMAwake 0.8112 0.1387 0.0352 0.0000 0.0000 0.0150NREM I 0.0664 0.4796 0.4276 0.0000 0.0000 0.0264NREM II 0.0219 0.0093 0.9430 0.0154 0.0004 0.0101NREM III 0.0044 0.0000 0.0868 0.7266 0.1822 0.0000NREM IV 0.0097 0.0130 0.0222 0.0073 0.9595 0.0000
REM 0.0247 0.0058 0.0102 0.0000 0.0000 0.9593
Establish Contextual Information
Awake Stage I
Stage II
Stage III
Stage IV
REM
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
Thank You!
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