An Automatic Sleep Scoring Toolbox: Multi-modality of

Polysomnography Signals’ Processing

Rui Yan

1, 2

, Fan Li

, Xiaoyu Wang

, Tapani Ristaniemi

and Fengyu Cong

1, 2

Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland

School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering,

Dalian University of Technology, 116024, Dalian, China

School of Information Science and Engineering, Dalian Polytechnic University, 116034, Dalian, China

Keywords: Polysomnography, Multi-modality Analysis, MATLAB Toolbox, Automatic Sleep Scoring.

Abstract: Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. To speed up the process

of sleep scoring without compromising accuracy, this paper develops an automatic sleep scoring toolbox with

the capability of multi-signal processing. It allows the user to choose signal types and the number of target

classes. Then, an automatic process containing signal pre-processing, feature extraction, classifier training (or

prediction) and result correction will be performed. Finally, the application interface displays predicted sleep

structure, related sleep parameters and the sleep quality index for reference. To improve the identification

accuracy of minority stages, a layer-wise classification strategy is proposed according to the signal

characteristics of sleep stages. The context of the current stage is taken into consideration in the correction

phase by employing a Hidden Markov Model to study the transition rules of sleep stages in the training dataset.

These transition rules will be used for logic classification results. The performance of proposed toolbox has

been tested on 100 subjects with an average accuracy of 85.76%. The proposed automatic scoring toolbox

would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.

1 INTRODUCTION

Sleep covers almost one-third of human lifespan.

Adequate and high-quality sleep is vital to our

physical and mental well-being (Pagel and Pandi-

Perumal, 2014). However, and likely because of our

ephemeral lifestyle in modern society, sleep disorder

complaints increase dramatically among people.

Assessing sleep behaviour and analysing the sleep

structure, therefore, become more and more crucial.

Up to now, the conventional visual scoring method is

still the main method in most clinical and sleep

research labs worldwide.

Visual scoring, mainly based on the rules of

Rechtschaffen & Kales (R&K) (Rechtschaffen and

Kales 1968) and the recently updated American

Academy of Sleep Medicine rules (AASM) (Berry et

al., 2012), requires at least one registered sleep

technologist (RST) who has sufficient expertise and

experience in sleep scoring. Generally, the annotation

of 8-h recording requires approximately 2-4 hours

(Hassan and Bhuiyan, 2016a), which is rather time-

consuming. Besides, visual scoring in some degree is

subjective, as the inter-scorer reliability among

trained technologists is less than 90% (Danker-Hopfe

et al., 2009). In contrast, automatic sleep scoring has

demonstrated advantages of cost-effective and

preferable scoring performance.

Electroencephalogram (EEG) signals are mainly

used in automatic sleep scoring since they contain

valuable and interpretable information resembling

brain activities (Boostani et al., 2017). According to

the morphological characteristics of EEG signals,

sleep EEG waves are mainly composed by α wave, 

wave,  wave and  wave, K complex, sleep spindles

and saw-tooth (Niedermeyer and da Silva, 2005).

These rhythm waves form the foundation of sleep

scoring. Some studies (Hassan et al., 2015; Hassan

and Bhuiyan, 2016b) tried to extract statistical and

spectral features from these rhythm waves to perform

an automatic sleep scoring. Cross frequency coupling

estimated between rhythm waves also showed high

classification accuracy (Dimitriadis et al., 2018).

Instead of traditional linear features, multiscale

entropy and autoregressive models for single-channel

EEG were employed in Liang et.al’ s study, obtaining

a good scoring performance (Liang et al., 2012).

Sleep is a complex process involving multiple

Yan, R., Li, F., Wang, X., Ristaniemi, T. and Cong, F.

An Automatic Sleep Scoring Toolbox: Multi-modality of Polysomnography Signals’ Processing.

DOI: 10.5220/0007925503010309

In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications (ICETE 2019), pages 301-309

ISBN: 978-989-758-378-0

301

Figure 1: The interface of sleep scoring toolbox.

organs. Signals recorded from different physical areas

change with the sleep cycle. The multi-modality

signals’ contribution to sleep scoring has been

explored in several studies (Gharbali et al., 2018; Yan

et al., 2019; Šušmáková and Krakovská, 2008). Özşen

concluded that as the sleep deepen, the frequency of

EEG signals attenuated gradually, along with rare eye

movements, low electromyography (EMG) activity

and slow heart rate (Özşen, 2013). Ebrahimi and his

colleagues found that under the control of

parasympathetic nervous system and sympathetic

nervous system, cardiovascular and respiratory

behaviours fluctuated with the alternation of sleep

stage (Ebrahimi et al., 2015). It has demonstrated that

features from multi-modality signals were beneficial

to the improvement of scoring accuracy (Boostani et

al., 2017).

Although there are many studies on automatic

sleep scoring, the available software and toolbox is

limited. Given that, this study aims to develop an

automatic sleep scoring toolbox with the capability of

multi-signal processing, see Figure 1. The main

contributions of this work are presented as following:

a) An automatic sleep scoring toolbox is proposed

which supports multiple sleep signals and two

data formats.

b) An interactive interface is provided which allows

the user to select the number of target classes,

change signal types and visualize various analysis

results.

c) A layer-wise classification strategy is proposed

which can significantly improve the classification

accuracy of minority stages without

compromising the accuracy of other classes.

d) A correction procedure is proposed to make

classification results logical.

The article is organized as follows: Section 2 explains

the details of experimental data and methodology of

this study. Section 3 demonstrates the performance of

proposed toolbox. Section 4 provides discussions of

results and limitations of this study. Finally, section 5

gives conclusions of this paper.

2 MATERIALS AND METHODS

2.1 System Overview

The proposed toolbox consists of a training module,

an offline prediction module, an online prediction

module and several parameter panels, as shown in

Figure 1. Their functions are briefly described in the

following lines. The specific model structure will be

introduced in detail in section 2.5.

Training Module: The objective of the training

module is to train a classifier based on the user’s

selection. The user can choose signal types and the

number of target stages as required. The software

automatically performs signal pre-processing, feature

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extraction and classifier training. The output of this

module is a trained model which can be used to

predict sleep structures.

Prediction Module: The aim of this module is to

predict sleep structure based on the predefined model

or user-specified model. The module automatically

checks if the user has trained a model, and allows the

user to determine if the predefined model is needed.

Once the model selected, the module automatically

processes the test data based on model parameters.

Finally, the application interface displays the

predicted sleep structure, related sleep parameters and

a sleep quality index as a reference to sleep quality. If

a hypnogram (e.g., labels scored by RST) is available

for the test data, the interface would display both the

hypnogram and predicted labels together, and

highlight the disagreement by pressing the button

named “Comp”.

Online Prediction Module: The module is similar to

the offline prediction process except for the real-time

updating results. The module can be connected to a

sleep monitoring device in order to realize the real-

time analysis of sleep signals and to visualize sleep

structures. The updated sleep signal will be saved as

a TXT file in storage.

2.2 Description of Experiment Data

The sleep data for this investigation was provided by

the Sleep Heart Health Study (SHHS) database. We

used only the first round (SHHS-1) due to its wide age

range. The recordings employed in this study were

selected by considering a Respiratory Disturbance

Index 3 Percent (RDI3P) < 5 to have near-normal

characteristics. Moreover, subjects did not use beta-

blockers, alpha-blockers, inhibitors, and did not

suffer documented hypertension, heart disease, or

history of stroke. Given that, a total number of 100

subjects were selected with the total duration of 816

hours and 43 minutes. The age of subjects ranged

from 40 to 54 years, with a mean value of 47 years

and a standard deviation of 4.3 years. Each record was

scored by the experienced research assistant or sleep

technologist according to the R&K rules. The sleep

recordings were segmented into 30-second per epoch

and labelled as wakefulness (W), non-rapid eye

movement stage (NREM, containing S1, S2, S3 and

S4) and rapid eye movement stage (REM). The

deepest NREM stage, namely S3 and S4, were

collectively referred to as “slow wave sleep” (SWS),

based on a prevalence of low-frequency oscillations

(Berry et al., 2012). A detailed description of SHHS

was given in the study (Quan et al., 1997) .

2.3 Pre-processing

For the predefined model and the following

experiments, four modalities of polysomnography

(PSG) signals were considered: EEG channels (C4-

A1 and C3-A2, following the 10-20 international

electrode placement system), two electrooculography

(EOG) channels (named: ROC, LOC), one submental

electromyography (EMG) channel and one

electrocardiography (ECG) channel. All the

aforementioned signals were fully included within the

evaluation process without discarding any recorded

segments, thereby to have a near-clinical situation.

In order to remove noise and artefacts, a notch

filter, a high-pass filter with a cut-off frequency of

0.3Hz and a low-pass filter with a cut-off frequency

of 30Hz were applied to the signals of EEG, EOG and

ECG. In terms of EMG, a notch filter, a high-pass

filter with a cut-off frequency of 10Hz and a low-pass

filter with a cut-off frequency of 75Hz were

performed. The whole night recordings were

smoothed by its mean value ±5×standard deviation to

remove the outliers. In order to eliminate individual

differences, the sleep signals were normalized to [-

100, 100]. Afterwards, all the signals were divided

into 30-second epochs, each epoch corresponding to

a single sleep stage.

2.4 Feature Extraction

The features, employed in this study, involves a

variety of traditional and modern characteristics

serving as distinctive markers for various psycho-

physiological states. They are summarized in Table 1.

Some of the parameters are introduced in the

following, and the others can be found in Yan et al.’s

research (Yan et al. 2019).

2.4.1 Time Domain Parameters

Some statistical parameters, such as minimum value,

maximum value, standard deviation, arithmetic mean,

variance, skewness, kurtosis and median are derived

from signal segments. These statistical parameters are

good indicators of the amplitude and distribution of

time series (Şen et al. 2014). Percentile analysis is

known as the most effective time domain measures

for EEG signals (Boostani et al. 2017). Hjorth

parameters (i.e., activity, mobility and complexity)

represent the signal power, the mean frequency and

frequency changes (Vidaurre et al. 2009).

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303

Table 1: Parameter list.

Type Feature Name

Statistical measures

Minimum Value (MinV), Maximum Value (MaxV), Arithmetic Mean(AM), Median(M),

Standard Deviation (SD), Variance(V), Skewness(S), Kurtosis(K), The 5

Percentile

(Pre5), The 25

Percentile (Pre25), The 75

Percentile (Pre75), The 95

Percentile

(Pre95), Hjorth Parameters（HA, HM, HC）, Zero-Crossing(ZC)

Spectral measures

Power Spectral Density(PSD), Mean Value of PSD (mPSD), Median Value of PSD

(mdPSD), Power Ratio(PR), Absolute and Relative Spectral Power (APSD, RPSD), Brain

Rate (BR), Spectral Centroid (Sc), Spectral Width (Sw), Spectral Asymmetry (Sa),

Spectral Flatness (Sk), Spectrum Flatness (Sf), Spectral Slope (Ss), Spectral Decrease (Sd),

Edge_D, Spectral Edge Frequency at 90% and 50%

Nonlinear measures

Mean teager energy (MTE), Mean Energy (E), Mean curve length (CL), SecD, The 4

Power,

Fractal measures Petrosian fractal dimension (PFD)

Entropy measures Spectral Entropy(SpE)

Mutual measures Coherence

2.4.2 Spectral Features

The calculation of spectral measures is based on

Fourier transform using hamming window in the time

domain. The following spectral measures are

considered.

Power spectral density is calculated based on the

following formula. Meanwhile, its mean value and

median value are also considered.

  









∗

/

)

where ω is the frequency, * representing the complex

conjugate, and N is the length of time series.

Spectral edge is defined as the frequencies

corresponding to 90% and 50% of the total spectral

power (Imtiaz and Rodriguez-Villegas 2014). The

difference between the two frequencies (edge_D) is

also considered.













 











)

where p is equal to 0.9 or 0.5, f

min

is 0.3Hz in terms of

EEG, EOG and ECG, and 10Hz in EMG.

Absolute and relative spectral power are obtained

from seven frequency bands of EEG, namely, 0.3-

4Hz (delta), 2-3.9Hz (K complex), 2-6Hz (saw-

tooth), 4-8Hz (theta), 8-12Hz (alpha), 14-30Hz

(beta), and 12-16Hz (spindle). Absolute spectral

power is spectral power within the specific frequency

bands. The relative value is defined as the ratio of the

absolute value to the total spectral power. The total

spectral powers of EEG, EOG and ECG signals are

computed within the range of 0.3-30Hz, and 10-30Hz

for EMG signals.

Power ratios are computed based on absolute

spectral powers in aforementioned frequency bands.

The following power ratios are computed: delta/theta,

delta/alpha, delta/beta, theta/alpha, theta/beta,

alpha/beta, alpha/(theta + delta), delta/(theta + alpha)

and theta/(beta + delta).

Brain rate estimates the EEG mean frequency

weighted over the brain spectrum distribution (Pop-

Jordanova and Pop-Jordanov 2005).

  













/ 







(3)

where  is the number of frequency bins,  the sub-

band, 



the power of the spectral distribution

corresponding to frequency band , and 



is the

frequency at bin .

Spectral centroid is defined as the frequency-

weighted sum of the magnitude spectrum of the signal

normalized

by its unweighted sum, indicating the

Layer1

RF1

Features

SWS S2 Others

RF2

OthersREM

RF3

WakeS1

Features

Layer2

Layer3

Figure 2: Layer-wise classifier.

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304

location of the spectrum centre (Hassan et al. 2015).

Spectral width is the wavelength interval over which

the magnitude of all spectral components is equal to

or greater than a specified fraction of the magnitude

of the component having the maximum value.

Spectral asymmetry represents the asymmetry in the

distribution of the spectrum of eigenvalues of an

operator. Spectral flatness, measured in decibels,

provides a way to quantify how noise-like a sound is

(Dubnov 2004). Spectrum flatness defines the

planeness properties from an audio signal’s spectrum,

which shows how the power spectrum of a signal

deviates from a frequency of a flat shape (Lazaro et

al. 2017). Spectral slope is a measure of the slope of

the spectral shape (Hassan et al. 2015). The steepness

of the decrease of the spectral envelope of the signal

with respect to its frequency is defined as spectral

decrease (Hassan et al. 2015). The detailed definition

of these parameters can be found in Chen et al.’s

study (Chen et al. 2018).

2.5 Classification

It is well-known that the distribution of epochs among

sleep stages are highly imbalanced. Unfortunately,

the traditional classifier is kind of sensitive to the

distribution of data sets. When instances of one class

in the training set vastly outnumber the instances of

other classes, the classifier inclines to classify

instances as belonging to the majority class and ends

up creating suboptimal classification models in the

process (Hassan and Bhuiyan 2016c). After studying

the characteristics of sleep stages, we find that the

REM, S1 and wakefulness present a certain similarity

leading to misclassification. For example, the level of

brain activity and eye movements increase in REM

stage which is similar to the waking period. In

addition, S1 is a transition phase of wakefulness and

sleep, along with the ambiguous neuronal oscillation,

that makes the detection of S1 is the most problematic

of the sleep stages. For S2 and SWS, with the

deepening of sleep, the activity levels of various

organs decrease to some extent.

Based on these characteristics of the sleep stages,

we develop a layer-wise classification strategy (See

Figure 2) which is used in this toolbox to train and

predict sleep structures. The strategy uses three

random forest classifiers. The first layer is a multi-

class classifier dividing the sleep sequences into

SWS, S2, and others. The second layer is a two-class

classifier, which aims to distinguish the REM stage

according to its lowest EMG activity and obvious eye

movements. The third layer discriminates the

characteristics of S1 and awake stage. Experiments

have confirmed that the structure can significantly

improve the recognition accuracy of the minority

sleep stages, such as S1, without significantly

reducing the classification accuracy of other classes.

2.6 Result Correction

Studies have found that sleep transition is not a

random process. However, the traditional classifier

can only give its decision according to the

information of the current stage, but can’t remember

the context. Therefore, a correction process is applied

to classification results. Firstly, the Hidden Markov

Model is used to learn the transition rules among

sleep stages in the training data. Then, the correction

rule can be derived according to these transition rules

and some natural characters of sleep. These rules refer

to the epochs prior to and posterior to the current

epoch. The development of correction rules is

inspired by the studies of Liang et al. (Liang et al.

2012) and

Li et al. (Li et al. 2018). More specifically,

the stage sequences, like [S

i-1

, S

i+1

], are smoothed

by the rules

proposed in the study (Liang et al.

2012) to correct some sudden changes in predicted

results. For the stage sequences that do not meet the

aforementioned smooth rules, the transition rules

derived from Hidden Markov Model will be used to

analyse the rationality of the stage transitions.

Table 2: Sleep parameters and its definition.

Slee

Parameters Definition

Time in be

From li

ht off to

ettin

Slee

eriod time From slee

onset to slee

end, in minutes

Slee

efficienc

Total slee

time / Bed time

Slee

onset latenc

From recordin

start to slee

onset, in minutes

REM latenc

From slee

onset to the occurrence of the first REM

eriod, in minutes

Sta

e shifts/h Number of slee

sta

e shifts after slee

onset

er hou

Wakin

times Number of awakenin

s after slee

onset

er hou

Wakin

time Wakefulness after slee

onset,

ercenta

e of slee

eriod time

Number of REM Number of REM

eriods

Sta

e time S

ecific sta

e time after slee

onset, in minutes

Sta

ercenta

ecific sta

e time in

ercenta

e of slee

eriod time

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305

*C: single-modality ECG; M: single-modality EMG; O: single-modality EOG; E: single-modality EEG; &: combination signals; 2 classes: wakefulness and

sleep (W&S); 3 classes: wakefulness, non-rapid eye movement sleep and rapid eye movement sleep (W&NREM&REM); 4 classes: wakefulness, light sleep

(containing S1 and S2), deep sleep (SWS) and rapid eye movement sleep (W&LS&DS &REM); 5 classes: W, S1, S2, SWS and REM

Figure 3: The classification accuracy for different signal fusions and target class.

Table 3: Selected features for distinguishing specific pair of sleep stages.

Sleep stages SWS-S2 SWS-S1 SWS-R SWS-W S2-S1 S2-R S2-W S1-R S1-W R-W

Top 15 features

Top1 C.Per25 C.Per25 C.Per25 M.ZC C.PFD O.ZC C.ZC O.ZC E.SPE O.ZC

Top2 M.ZC C.Per75 C.Per75 C.ZC E.PSD O.Sw M.ZC O.Per75 C.PSD O.Per75

Top3 C.Per75 M.ZC C.Per95 M.PFD C. mPSD E.PR E.SPE E.K O.Ss O.Per25

Top4 C.ZC C.Per95 M.Per25 C.Per25 E.PR E.PFD C.PFD O.Per25 O.Sf O.PFD

Top5 C.Per5 M.Sf M.ZC C.PFD C.HM O.edge90 O.Sf O.PFD E.MTE M.ZC

Top6 C.Per95 E.PSD C.PSD E.PSD M.PFD C. mPSD M.PFD O.HC O.MTE C.ZC

Top7 C.K C.Per5 E.Per95 C.Per75 O.Sd C.K O.Ss O.HM C.ZC E.Per5

Top8 O.K M.Per25 C.Per5 M.Sf M.Sf O.Sd E.PFD O.SPE C.Ss O.Ss

Top9 E.Per5 O.PFD O.Power4 O.edge.D E.PFD O.PFD C.HM O.K O.MaxV O.RPSD

Top10 M.Per25 E.RPSD M.Per75 O.Sk O.BR E.S C.PSD O.K E.D O.CL

Top11 M.K C.ZC E.PR E.PR O.PFD O.BR E.edge90 O.CL C.Sf C.PSD

Top12 O.Per75 C.HM C.K E. mdPSD C.ZC C. mdPSD E.PR O.RPSD O.RPSD E.CL

Top13 E.Per25 M.HM C.HA M.HM O.edge90 O.HC C.R R O.SecD E.MaxV O.SecD

Top14 M.HM E.RPSD O.Sf E.PR C.mPSD O.S C.mPSD O.MaxV M.ZC O.Sf

Top15 E.K O.edge.D C.PFD E.PR O.Sw E.PR M.HM O.Sc C.PFD E.Sc

EEG features (colour: yellow); EOG features (colour: green); EMG features (colour: red); ECG features (colour: blue).

2.7 PSG Sleep Quality Index

Usually, sleep quality is evaluated by the

standardized questionnaire, such as the Pittsburgh

Sleep Quality Index (PSQI), the Berlin

Questionnaire, and so on. These self-report

questionnaires

are subjective, and can be

easily exaggerated or minimized by the person

completing them. Furthermore, some items of

questionnaires are challenging to self-evaluation. For

example, the PSQI needs to evaluate the time it takes

to fall asleep and the actual sleep time per night. Some

papers claimed that the correspondence between the

objective measurement and a person’s subjective

assessment of the sleep quality is surprisingly small,

if existent (Sohn et al., 2012). In order to overcome

the uncertainty of subjective assessment, the toolbox

proposed a sleep quality index. The algorithm will

calculate various sleep parameters (summarized in

Table 2) according to the predicted sleep stages.

Based on these sleep parameters, PSG sleep quality

index is statistically calculated and displayed in a bar

in the lower-right corner of the interface. Detailed

sleep parameters can be obtained by pressing the

button “Detail”.



E&C

E&M

E&O

C&M&O

E&M&C

E&C&O

E&M&O

E&M&O&C

E&C

E&M

E&O

C&M&O

E&M&C

E&C&O

E&M&O

E&M&O&C

E&C

E&M

E&O

C&M&O

E&M&C

E&C&O

E&M&O

E&M&O&C

E&C

E&M

E&O

C&M&O

E&M&C

E&C&O

E&M&O

E&M&O&C

100

Accuracy(%)

2 classes: W&S 3 classes: W&NREM&REM 4 classes: W&LS&DS&REM 5 classes

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3 PERFORMANCE ASSESSMENT

3.1 Influence of Signal Types

In order to explore the relationship between signal

types and classification accuracy, we performed a

greedy search for several signal fusions referring to

different target classes. The result was shown in

Figure 3 where the column denoted the mean

accuracy of 10-fold cross-validation and the bars

represented the standard deviation. Four categories

were considered, highlighted in different colours in

Figure 3. For each category, twelve signal fusions

were listed along X-axis where signals’ names were

abbreviated to its middle letter.

Figure 3 depicted the uncertainty or variation of

classification accuracy under each condition.

Collectively, with the enrichment of signal types, the

mean value of accuracy increased, and the uncertainty

decreased to some extent. More specifically, Figure 3

indicated that the required signal types varied with the

number of target classes. If sleep recordings were

classified into two classes, namely wakefulness (W)

and sleep (S), all considered signal fusions gave

satisfactory results. With the increasing number of

target classes, the number of required signals

increased accordingly.

From the perspective of signal types, the signal

fusions containing EEG signals showed better

identification accuracy, indicating a crucial role of

EEG signals in sleep scoring. Furthermore, the

discriminative information provided by ECG and

EMG channels was inferior to that from EEG and

EOG signals.

3.2 Feature Evaluation

To further elucidate the contributions of features and

signals, the important features, measured by their

contribution to distinguishing each pair of sleep

stages, were derived from random forest classifier.

The top 15 features were shown in Table 3, where

features sorted in descending order of discriminative

capability. As can be seen from Table 3, the features

from EEG contributed to the recognition of most

stages. Meanwhile, ECG features demonstrated its

contribution to the discrimination of SWS from the

others. For EOG signal, its features were good at

distinguishing REM stage and wakefulness. In terms

of feature types, the top 15 features indicated that the

optimal feature subset was a fusion of statistical

measures (e.g. Percentiles, Hjorth parameters, Zero-

Crossing), spectral measures (e.g. spectral edge,

power spectral density), entropy measures (e.g.

spectral entropy), fractal measures (e.g. Petrosian

fractal dimension) and nonlinear measures (e.g. mean

curve length, the 4th Power).

4 DISCUSSION

PSG, the golden standard for measuring sleep

qualitatively, is a traditional technology which is

time-consuming and has barely changed over the

years. Burgeoning public interest in sleep quality

improves a strong impetus for a robust, easily

implemented and rapid sleep scoring system. Limited

toolbox or software is available for automatic sleep

scoring, although there are many theoretical

researches in this field. In previous studies, some

portable devices were developed based on ECG and

respiration. For example, Hermawan et al.

(Hermawan et al., 2012) developed a real-time sleep

stage classification device which classified sleep

recordings into 2 stages (wakefulness and sleep) with

an average precision of 0.941. Recently, some deep

learning-based scoring tools sprouted out, such as

SLEEPNET (Biswal et al., 2017) and SeqSleepNet

(Phan et al., 2019). The classification accuracy of

these deep learning-based tools was about 0.85 with

the support of tremendous training data and highly

configured computer (like GPU or server).

Compared with previous studies, the proposed

toolbox provides comparable precision and greater

freedom. The toolbox, based on MATLAB, allows

users to select the available signal types and the

number of target classes according to their condition

and need. Meanwhile, it supports two popular data

formats (MAT file and EDF file) that make data

transfer easy. This offline prediction module is

helpful for researchers, especially the newcomers in

this field, to accelerate their understanding of sleep

structures. It can also be used in clinic to speed up the

annotation of PSG records, thus alleviating the

burden of the physicians. The online prediction

module provides the potential to control sleep tasks

automatically by combining the toolbox with sleep

experiments.

Even though our results are encouraging, our

model still has several limitations. One of them is that

the performance of proposed toolbox is affected by

the data property. As our model learns from training

data, it might not perform well when the trained

model is applied to the data with different properties.

For example, a scoring model trained by healthy

subjects may not perform well for the analysis of

patients' sleep structure. To achieve better results in

An Automatic Sleep Scoring Toolbox: Multi-modality of Polysomnography Signals’ Processing

307

that condition, the model might have to be re-trained

or fine-tuned.

5 CONCLUSIONS

This paper proposed an automatic sleep scoring

toolbox that supported four types of sleep signals and

two data formats. The toolbox provided an interface

for user-friendly operation. Sleep recordings could be

automatically analysed to reveal multiple sleep

parameters and sleep quality index. A layer-wise

classification strategy was proposed to improve the

classification accuracy of minority stages. In

addition, a Hidden Markov Model was used to make

classification results logic. Compared with manual

scoring, the proposed automatic scoring toolbox is

cost-effective, which would alleviate the burden of

the physicians, speed up sleep scoring and expedite

sleep research.

ACKNOWLEDGEMENTS

The authors would like to thank the SHHS for

providing the polysomnographic data. This work was

supported by the scholarships from China

Scholarship Council (Nos. 201606060227).

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