SLEEP STAGE SCORING USING CNNs AND RNNs

SUMMER INTERNSHIP PROJECT REPORT

Deepak S 2016103516

Bhargav K 2016103514

Srikrishna B 2016103068

Ambika S 2016103007

COLLEGE OF ENGINEERING, GUINDY

ANNA UNIVERSITY: CHENNAI 600 025

MAY 2018

1. PROBLEM STATEMENT

Analyzing the sleep of patients, known as sleep stage scoring, is an expensive

and time-consuming process that cannot scale to a large number of people.

Using wearable EEG and automating sleep stage scoring is a potential solution

to this problem. In this project, a method to automate sleep stage scoring using

deep learning is brought forward.

2. FUNCTIONAL DESCRIPTION

Millions of people use smartphone apps, bedside monitors, and wearable items

including bracelets and smart watches to informally collect and analyze data

about their sleep. Smart technology can record sounds and movement during

sleep, journal hours slept, and monitors heartbeat and respiration. Using a

companion app, data from some devices can be synced to a smartphone or

tablet, or uploaded to a PC. The preliminary purpose though, is to do the same

using a person’s brain signals as opposed to recording sounds or movements.

The deep learning model consists of two neural networks that work

collaboratively to score sleep stages. The first network works on extracting time

invariant features which are then used by the second network to learn sleep

stage transition rules. From this learning experience, the network becomes

equipped to predict or score sleep stages from any input that’s given to it. The

output obtained can thus be used for sleep pattern analysis by experts who can

use these results to detect sleep-related disorders or the ill effects that irregular

or disrupted sleep patterns have on other bodily or cerebral functions. If

nurtured, the model will be mature enough to tabulate information on a timely

basis as against number of samples which will be helpful in deepening the scope

for research and may also greatly save manual labor and resource expenditure.

On an application level, it can be used to set an alarm by tapping information

from sleep stages that are classified against time intervals. This could paint a

clear picture in terms of when a person should be woken up within a desired

time frame. Using this information, the user is also informed about how much

more sleep he needs get in order to make up for all the time that he would have

spent sleeping in restlessness.

3. WORKFLOW

Fig 3.1 General workflow

3.1 SLEEP EDF DATASET

This collection of polysomnograms and hypnograms comes from two studies;

data from a study of age effects on sleep in healthy subjects and data from a

study of Temazepam effects on sleep. However, only the former was chosen to

compute the results. The PSG files are whole-night polysomnographic sleep

recordings containing EEG (from Fpz-Cz and Pz-Oz electrode locations), EOG

(horizontal), submental chin EMG, and an event marker; the SC* PSG.edf.

Files often also contain oro-nasal respiration and rectal body temperature.

The HYP files contain annotations of the sleep patterns that correspond to the

PSGs. These patterns (hypnograms) consist of sleep stages W, R, 1, 2, 3, 4, M

(Movement time) and ‘?’(not scored). All hypnograms were manually scored by

well-trained technicians. The SC files were obtained in a 1987-1991 study of

age effects on sleep in 79 healthy Caucasians aged 25-101, without any sleep-

related medication.

Two PSGs of about 20 hours each were recorded during two subsequent day-

night periods at the subjects' homes. The subjects currently included here,

numbers 00 through 19, are 10 males and 10 females, and were 25-34 years old

at the time of the recordings. Subjects continued their normal activities but wore

a modified Walkman-like cassette-tape recorder.

3.2 DATA PREPROCESSING

Pre-processing is a technique used to cleanse a raw data set. As the data had

been gathered as an amalgamation of signals from various cerebral cross-

sections, they had to be scripted to a format that is consumable by the neural

network. Hence, this technique had been adopted to differentiate the unknown

sleep stages from the known and discard all ambiguous data or in broader terms,

the ones corresponding to the minority sleep stages. Hence, the data along with

their respective labels were extracted from the EDF files and were later

transformed into NumPy arrays.

Fig 3.2 Input data

Fig 3.3 Data extraction

Fig 3.4 Label extraction

3.3 MODEL DESIGN

There are many applications that are equipped to predict a person’s sleep quality

based on their movements in bed or by recording their breathing patterns.

Lesser known are methods that can perform detailed studies using one’s brain

signals. This was the motivation behind scoring sleep stages using deep

learning. The neural network was used to accomplish this task as electrical

signals in general are sensitive and demand to be analyzed carefully. The model

learns how to differentiate between the five main sleep categories and classifies

a cluster of input samples by making an educated guess.

3.4 TRAINING

Training is the process by which a model learns how to coherently identify or

classify the data that it sees. To draw a parallel, it works similar to the human

brain where one learns how to recognize worldly entities by visually perceiving

them. Similarly, the model is trained to classify sleep stages by learning which

frequencies and amplitudes are best suited to each of the five main sleep

categories.

3.5 PREDICTION

Prediction is the process by which the model uses the information that it has

learnt in the past to classify unseen data. The model scores sleep stages using

the knowledge that gains from visiting the training inputs so as to accomplish

this feat.

4. RESULTS AND SCREENSHOTS

4.1 DATA PREPARATION

The prediction is to be made on data formatted as numpy arrays. For this, the

EDF files of arbitrary subjects are pre-processed and converted to the said

format. This data is then given as input to the neural network. The extraction of

features happens in the two CNN layers while the sleep transition stages are

learnt by the LSTM layers in the latter half of the algorithm. The working is

explicitly portrayed in the pictures given below.

Fig

4.1 CNN layer 1

Fig 4.2 CNN layer 2

Fig 4.3 Adding the CNN layers

Fig 4.4 LSTM cells

4.2 EVALUATION CRITERIA

Confusion Matrix

A confusion matrix is a table that is used to outline the efficiency of a

classification model on a set of test data for which the true values are known. It

contains information about actual and predicted classifications performed by a

classification system. Each column of the matrix depicts an instance in a

predicted class while each row depicts an instance in an actual class.

The confusion matrix for a given number of samples for two subjects is shown

in Figure 4.5

Recall

Recall measures the proportion of positives that are correctly identified as such.

It can be thought of as a measure of classifier’s completeness.

Precision

Precision is the fraction of relevant instances among the retrieved instances. It

can be thought of as a measure of a classifier’s exactness.

Accuracy

Accuracy is the number of correct predictions made out of the total number of

predictions made.

● True Positive (TP) : number of correctly recognized observations for

class a class C.

● False Positive (FP): number of observations that were incorrectly

assigned to a class C.

● True Negative (TN) : number of correctly recognized observations that do not belong to a class C.

● False Negative (FN) : number of observations that were not recognized as belonging to a class C.

Fig 4.5 Confusion matrix

The average accuracy is found to be : 89.8%

The predicted output is plotted in Figure 4.6 with the number of samples along

X-axis and classes along the Y-axis

Fig 4.6 Predicted output with X axis as Samples and

Y axis as Sleep Stage labels