1. Automatic Sleep Stage Classification using a Neural Network Algorithm
Zoe Cohen
Supervisor: Prateek Gundannavar, Principal Investigator: Arye Nehorai, PhD
Department of Electrical and Systems Engineering
Abstract: For this project I developed and tested a neural network algorithm for the purpose of performing automatic sleep stage classification. Sleep is typically classified into six different stages: wake, N1, N2, N3/N4, and REM (rapid eye movement). The classification is based on various standards set by the American Academy of Sleep Medicine (AASM) and requires a trained sleep technician. In this project I wrote a neural network algorithm to perform classification based on these standards, thus making the process automatic. The neural network algorithm was developed by improving and building on previous iterations, the final result being a classifier capable of discriminating between five different classes with 80.82% accuracy.
Introduction:
Feature Extraction:
Results:
Sleep scoring is the procedure by which various biological signals (EEG, EMG, EOG) are examined to determine the sleep stage of a patient. It is valuable for its diagnostic potential as abnormalities in sleep can serve as biomarkers for various diseases, such as Alzheimer’s. However, the procedure of sleep scoring requires the recording of complex biological signals and a trained sleep technician to review the recordings. A neural network algorithm would eliminate the need for a technician, thus simplifying the process and making sleep scoring a more attractive diagnostic tool.
Below are the results of a neural network performing multiclass classification using the features listed in the table at left. (Regularization parameter λ=1.4)
Time:
Mean
Median
Maximum
Kurtosis
Variance
Skewness
# Zero Crossings
Hjorth mobility and complexity
Frequency:
Factoral exponent
Dominant freq
Spectral edge freq
Average freq
Total bandpower
Spectral moments
Power ratios (α, β, γ, δ)
Entropy:
Spectral entropy
Renyi entropy
Relative spectral entropy
Fisher information
Accuracy = 80.82%
Actual distribution: W N1 N2
N3/N4
REM
# per class
1828
573
3257
113
842
Predicted distribution:
Sleep stage scoring criteria: W N1 N2
N3/N4
REM
1649 35
100 20
172 80
236 63
174 27
3000 6
121 71 40 1 47 15
576
Stage
Scoring Criteria (epoch length = 30 sec) W
>50% alpha (8-13 Hz) or low voltage, mixed freq N1
<50% alpha activity; low voltage, mixed freq; slow eye movements and vertex sharp waves N2
Sleep spindles and K complexes; <20% high voltage (>75 µV), low freq (<2 Hz)
N3/N4
>20% high voltage (>75 µV), low freq (<2 Hz)
REM
Low voltage, mixed freq; rapid eye movements; muscle atonia
Neural Network Classification:
a1(3)
Feature Selection:
Principal Component Analysis (PCA) allows for the dimension of the feature vector to be reduced without compromising accuracy:
a1(2) x1
h(x) = g(z(3))
Design Overview:
Preprocessing
Feature Extraction
Classification with Neural Network
ah(3) xn
ah(2)
Feature Selection
Preprocessing:
g(x) is the activation of each unit in the network:
Σg(x)
All EEG time-series data were filtered using a tenth-order Butterworth bandpass filter in order to remove any frequency components outside the range of Hz. The data was also subjected to a moving average filter and a notch filter at 60 Hz.
Conclusion:
The accuracy achieved in this project, %, is a good start, but not ideal. This value can be improved with better feature extraction techniques, particularly those that take advantage of the characteristic waveforms found in N2. There is no doubt that a high performing automatic sleep scoring algorithm would be of tremendous value to the medical community.
Training the neural network:
Randomly initialize weights Θ(1), Θ(2)
Implement forward propagation to compute h(x)
Compute error using cost function
Use back propagation to compute derivatives of cost fxn
Increment weight values by cost function derivatives