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 DevicePortable Device

DetectionAlgorithm DetectionAlgorithm

PredictionAlgorithm PredictionAlgorithm

Expert SystemExpert System

Medication Allocation

Medication Allocation

Activity Planning

Activity Planning

GUI forPatient GUI forPatient

GUI forDoctorGUI forDoctor

StoredData

StoredData

ObjectiveEvaluation of Patient Condition

Doctor’s New Instructions

Current Condition

Medication & Activity

EstimatedFuture Condition

Suggested Actions

DetectionAlgorithm 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%

FYDP

Approach 1 Approach 2

Method MLP HMM

Features Frequency Hjorth

Output Awake/AsleepAwake, 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

AwakeStage

IStage

IIStage

IIIStage

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!