Automated sleep scoring using unsupervised learning of - PowerPoint PPT Presentation

Automated sleep scoring using unsupervised learning of meta-features DD221X: Degree project in Computer Science May 3rd 2016 Sebastian Olsson

What is sleep scoring? Judgments about a sleeping individual ● ● Sleep stages: REM, N1, N2, N3, Awake

Hypnogram Sleep stage graph ●

Electroencephalogram (EEG) ●

Electroencephalogram (EEG)

Electroencephalogram (EEG) N3

Electroencephalogram (EEG) N3 N1

Electroencephalogram (EEG) N3 N1 REM

Electroencephalogram (EEG) N3 N1 REM Awake

Automated sleep stage scoring

Automated sleep stage scoring

Automated sleep stage scoring Compare 100 % agreement

A Problem statement

B Problem statement

B Problem statement Deep belief net (DBN) ● ● Compare approaches

Method

Data SHHS1 ● ● 10 records ● Annotations

Segmentation 30 s

Feature extraction Mean Variance (12, 0.8) (15, 0.3)

Features Mean ● ● Variance ● Skewness Kurtosis ● Hjorth mobility ● ● Hjorth complexity ● Amplitude

Partitioning 75 % training set ● ● 25 % test set

Feature selection Find a decent combination of features ● ● Strip away unwanted features ○ Curse of dimensionality Inspired by Löfhede [1] ● ● Genetic algorithm ○ Roulette-wheel selection ○ Mutation rate: 0.2 ○ Crossover rate: 1.0 ○ Number of generations: 5 ○ Population size: 5 ○ Chromosome length: 7 ● “Cross-validation”

Feature classification Support vector machine (SVM) ● ○ Linear kernel Radial basis function (RBF) kernel ○ ● Trained using the training set ● Evaluated using the test set

Unsupervised DBN processing DBN ● ○ Two stacked Restricted Boltzmann machines (RBM) Based on Längkvist [2] ● 1. Pre-training 2. Unsupervised fine-tuning with backpropagation 3. Propagate the feature space through the network

Unsupervised DBN processing Three meta-features ● ● Appended to vector: (x 1 , ..., x 7 ) ↦ (x 1 , …, x 7 , m 1 , m 2 , m 3 )

Evaluation 10 ∙ 3 ∙ 2 ∙ 2 = 120 evaluations ● with/without # records DBN # re-runs linear/RBF kernel

Results

Results Scorer A, linear kernel Scorer B, linear kernel Scorer A, RBF kernel Scorer B, RBF kernel

Results

Results

Conclusion Unsupervised DBN processing did not help ● ● Effect too small to be noticable ● Approach too specific to arrive at a general conclusion

Future work Simplify ● ○ Skip feature selection Append/replace ● Try different parameters, e.g. ● ○ Number of meta-features (output nodes) Number of RBMs ○ ○ Number of hidden layer units Epochs ○ ○ Initial biases

References [1] J. Löfhede. (2009). The EEG of the neonatal Brain - Classification of ● Background Activity . ● [2] M. Längkvist. (2012). Sleep Stage Classification Using Unsupervised Feature Learning .

Images ● Licensed under CC BY-SA 3.0: ○ https://commons.wikimedia.org/wiki/File:HYPNOGRAM_created_by_Natasha_k.jpg ● Licensed under CC BY-SA 4.0: ○ https://commons.wikimedia.org/wiki/File:Sleep_scoring.png ● Public domain: ○ https://commons.wikimedia.org/wiki/File:1st-eeg.png ○ https://pixabay.com/en/scientist-professor-man-researcher-28748/ ○ https://pixabay.com/en/artificial-intelligence-155161/ ● Licensed under CC0 1.0: ○ All remaining images

The End