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Final Project Classification of Sleep data Akane Sano akanes@mit.edu Affective Computing Group Media Lab

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Polysomnography Multi-parametric test to evaluate sleep

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Data and Labels Data: Healthy students (N=7) Electroencephalogram(EEG) 100Hz Electro dermal activity(EDA) 32Hz Motion 32Hz Labels : sleep stages (every 30s) (Wake, REM, NonREM1-4, Movements/Noise) -> Wake, REM, Non-REM, Movements/Noise

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Sleep Stage NREM

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Sleep Stage EEG Motion EDA [Hz]

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Questions Which features are the best to estimate sleep stage? How accurate can we estimate sleep stage from EDA and motion?

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data SubjectWAKEREMNREMMOVEMENT Q3171245270 R151417193 S461847149 T101896300 U512097093 V382037035 W4917973532 %8.018.872.40.8

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Features Recorded signals were segmented into 30s window Computed with MATLAB EEG : Frequency Energy :0.5-4,Hz :4-8 :8-13Hz :13-40Hz :40-50Hz) Motion: Amplitude Standard Deviation Zero-crossing Frequency Energy EDA : Amplitude (Normalized) Standard Deviation # of peaks Gradient Frequency Energy

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Red Non-REM, blue Wake pink REM black M

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Methods Using Matlab k Nearest Neighbors (k=1-199) Support Vector Machine (libSVM) Linear Polynomial Radial Basis Function Dynamic features (still working) Neural Network (n=2, 4, 6, 8, 10, 20) Hidden Marcov Model / Baysian Network (still working) Gaussian Mixture Model (HMM toolbox Errors, Bayes Net toolbox : Errors) Discrete Model (HMM toolbox Errors)

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Methods (cont.) Leave one subject out Compare Accuracy Classification Methods Features

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Results Accuracy [%] (SVM) SVM FeaturesLinear PolyRGB ALL73.272.471.5 EEG71.372.669.9 EDA72.6 MOTION72.6 70.4 W30.7 REM32.1 NREM91.2 MOVE0 misclassified to NREM W0 REM0 NREM100 MOVE0 predicted WRNM true W0.30.10.60.0 R 0.30.70.0 NR0.00.10.90.0 M0.30.10.50.0

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Results Accuracy [%] (kNN) featureskNN ALL76.0n=137 EEG76.7n=9 EDA70.1k=199 MOTIO N 70.3n=197 Improved For ALL, EEG, but decreased for EDA and MOTION

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Results Accuracy [%] Neural Network Node # Features ALLEEGEDA MOTIO N 2 82.885.272.672.5 4 84.985.672.573.1 6 84.986.172.973.1 8 85.186.873.073.3 10 85.586.573.773.4 20 86.088.274.073.3 Improved!

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Another Better Feature? Added Elapsed time (0:Record Start-1:End) SVM kNN LinearPolyRGB ALL73.572.171.7ALL76.1 EEG72.272.670.9EEG75.5 EDA72.671.771.9EDA68.6 MOTIO N 72.6 MOTION70.0 0.3% UP! Improved For ALL, EEG, but decreased for EDA and MOTION

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Neural Network (Elapsed Time added) Node # Features ALLEEGEDAMOTION 284.383.173.073.8 486.685.772.473.6 686.785.073.974.3 887.485.774.474.0 1087.586.474.274.3 20 87.689.274.8 Improved!

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Best classifier Neural Network(n=20) EEG with elapsed time Precited WRNM True W0.70.10.30.0 R 0.80.20.0 NR0.00.10.90.0 M0.30.00.20.5

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Summary Node # Features ALLEEGEDA MOTIO N SVM73.272.6 kNN 76.076.770.170.3 Neural 86.088.274.0 73.4 SVM with time 73.572.6 kNN with time 76.1 75.568.670.0 Neural with time 87.689.274.8

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Conclusion EDA and Motion showed less accuracy than EEG and ALL Wake, REM, Movement were misclassified to N- REM Neural Network showed the best accuracy Elapsed Time might be effective Future Work Temporal model Features