The Journal of Engineering Research (TJER), Vol. 17, No. 1, (2020) 24-33 

DOI:10.24200/tjer.vol17iss1pp24-33 

*Corresponding author’s e-mail: abhossen@squ.edu.om

IDENTIFICATION OF OBSTRUCTIVE SLEEP APNEA USING 
ARTIFICIAL NEURAL NETWORKS AND WAVELET PACKET 

DECOMPOSITION OF THE HRV SIGNAL 

Sarah Qasim Ali and Abdulnasir Hossen * 
 Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University  

Oman, P. O. Box 33, Al-Khoudh, 123 Muscat, Oman. 

Abstract: The advancement of telecommunication technologies has provided us with new promising alternatives 
for remote diagnosis and possible treatment suggestions for patients of diverse health disorders, among which is 
the ability to identify Obstructive Sleep Apnea (OSA) syndrome by means of Electrocardiograph (ECG) signal 
analysis. In this paper, the standard spectral bands’ powers and statistical interval-based parameters of the Heart 
Rate Variability (HRV) signal were considered as a form of features for classifying the Sultan Qaboos 
University Hospital (SQUH) database for OSA syndrome into 4 different levels. Wavelet packet analysis was 
applied to obtain and estimate the standard frequency bands of the HRV signal. Further, the single perceptron 
neural network, the feedforward with back-propagation neural network and the probabilistic neural network have 
been implemented in the classification task. The classification between normal subjects versus severe OSA 
patients achieved 95% accuracy with the probabilistic neural network. While the classification between normal 
subjects versus mild OSA subjects reached accuracy of 95% also. When grouping mild, moderate and severe 
OSA subjects in one group compared to normal subjects as a second group, the classification with the 
feedforward network achieved an accuracy of 87.5%. Finally, when classifying subjects directly into one of the 
four classes (normal or mild or moderate or severe), a 77.5% accuracy was achieved with the feedforward 
network. 

Keywords: Sleep Apnea; Identification; Classification; HRV; Wavelet Packet Decomposition; Neural Networks. 

 تحديد انقطاع التنفس االنسدادي أثناء النوم باستخدام الشبكات العصبية االصطناعية 
HRV  إلشارةوتحليل حزمة األطوال الموجية   

  *وعبدالناصر حسين سارة قاسم علي

أتاح لنا التقدم العلمي في مجال تقنيات االتصال بدائال جديدة واعدة للتشخيص عن بعد واقتراحات عالجية   الملخص: 
محتملة لمرضى مختلف االضطرابات الصحية. ومن ضمن هذه التقنيات القدرة على كشف مرضى متالزمة انقطاع التنفس  

النطاقات   ). اعتبرت في هذه الدراسة  قوىECGادي أثناء النوم عن طريق تحليل إشارة التخطيط الكهربائي للقلب (االنسد 
) سمة من سمات  HRVالطيفية القياسية واإلحصائيات المقسمة على فترات زمنية إلشارة تغير معدل نبضات القلب (

تصنيف وتقسيم قواعد بيانات مستشفى جامعة السلطان قابوس المتعلقة بمتالزمة انقطاع التنفس االنسدادي الى اربع 
اللتي بنيت عليها الدراسة. وقد  HRVتقدير نطاقات التردد القياسية إلشارة مستويات. تم إستخدام حزمة األطوال الموجية ل

تم تصميم كل من الشبكة العصبية الحسية الوحيدة، و الشبكة العصبية ذات التغذية األمامية واالنتشار الخلفي، والشبكة 
حاء والمرضى من ذوي األعراض % في التمييز بين األص95العصبية اإلحتمالية في مهمة التصنيف. حقق اإلختبار دقة 

% في التفرقة بين األشخاص األصحاء واألشخاص المصابين 95الحادة باستخدام نموذج الشبكة العصبية اإلحتمالية, ودقة 
بالمتالزمة ويعانون من أعراض بسيطة. أما عند تشكيل عينة تشمل مرضى يعانون من أعراض مختلفة الشدة (بسيطة 

أخرى من أشخاص أصحاء فإن نموذج الشبكة العصبية ذات التغذية األمامية واإلنتشار الخلفي  ومتوسطة وشديدة) وعينة 
 واإلنتشار األمامية   التغذية  ذات   %, وتمكن نموذج الشبكة العصبية87.5تمكنت من التفريق بين العينتين بدقة بلغت 

  .%77.5من التمييز بين األنواع األربعة بدقة بلغت   الخلفي

نقطاع التنفس أثناء النوم؛ الكشف؛ التصنيف؛  معدل تغير نبضات القلب؛ تحليل حزمة امتالزمة  مفتاحية:الت كلماال
  األطوال الموجية؛ الشبكات العصبية اإلصطناعية.



Identification of Obstructive Sleep Apnea Using Artificial Neural Networks and Wavelet Packet Decomposition 

25 

1. INTRODUCTION

A sleep disorder occurs when the pattern of sleep is 
interrupted repeatedly during sleep (Kumar V 2008). 
Lack of sleep results in abnormalities in functions of 
the brain leading to cognitive impairment, changes in 
mood, low productivity, daytime sleepiness and 
abnormal hormonal rhythms (Wilson S 2016). Sleep 
apnea is a chronic disease that affects the health and 
productivity of individuals (National Heart, Lung and 
Blood Institute 2016) since it causes abnormal sleep 
pattern. Obstructive Sleep Apnea (OSA) is the most 
common type of sleep apnea followed by Central 
Sleep Apnea (CSA) and Mixed Sleep Apnea (MSA). 
OSA affects 3~4% and 2% of middle-aged men and 
women respectively (Lee W et al. 2008). Unlike CSA 
which results from heart failure or brain disorders, 
where the brain fails to control breathing leading to 
cessation of all respiratory airflow and movements, 
OSA results from a repeated process of complete or 
partial collapse in the upper airways of the respiratory 
system ranging from few seconds (minimum 10 sec) 
to minutes despite the ongoing brain efforts for the 
body to breath.  The OSA events may occur more 
than 30 times and up to 100 times per hour. MSA on 
the other hand, is a mixture of CSA and OSA in the 
same individual. (American Academy of Sleep 
Medicine 2001; National Sleep Foundation 2016). 
OSA has been related to some serious co-morbidity 
such as cardiovascular diseases, arrhythmia, strokes, 
obesity, depression, certain types of hypertension and 
type 2 diabetes mellitus (Global Leaders in Sleep and 
Respiratory Medicine 2013; Xie W et al. 2014). There 
are several screening methods used for OSA detection 
to find evidence of its presence in patients for further 
evaluation. These methods depend on psychometric 
and physical evaluations during the routine health 
check-ups. Polysomnography (PSG) sleep study is the 
gold standard test for sleep apnea diagnosis. This test 
requires the patient to sleep in a sleep laboratory 
while attached using several electrodes to many 
devices for different biometric measures carried out 
by qualified sleep physicians overnight. The severity 
of sleep apnea is commonly determined by an Apnea-
Hypopnea Index (AHI) which represents the number 
of obstructive, central, mixed and hypopnea episodes 
occurring during an hour of sleep (American 
Academy of Sleep Medicine 2001). If the AHI ranges 
between 0-5 apneic episodes during an hour of study 
time or sleep then the condition is considered normal. 
An index of 5-15 is considered mild while an index of 
15 – 30 is considered moderate and if the index is 30 
or above, the subject is considered to have a severe 
degree of sleep apnea (Global Leaders in Sleep and 
Respiratory Medicine 2013). Electrocardiography 
(ECG) is a method used to measures the electrical 
activity of the heart by placing electrodes on different 
parts of the body (WebMD 2016). A normal sinus 
rhythm reflects the normal activity of the heart while 

pumping blood to perform the sympathetic and 
parasympathetic activities (UCDavis Health System 
2016). A typical ECG signal is produced when the 
heart chambers contract and expand to pump 
oxygenated-blood throughout the body and circulate 
the desaturated blood to the lungs. Fig. 1 (a) shows a 
typical ECG signal (Sharma S. et al. 2019).  

Heart rate (HR) is a simple measurement that 
indicates the average number of heart beats during a 
certain time period (usually, a minute). A low HR 
reflects resting status while high HR indicates stress 
or exertion (Moore J 2016). Heart Rate Variability 
(HRV) on the other hand is a measure of the time 
variability in milliseconds between consecutive beats 
or correspondingly in the instantaneous HR. In other 
words, variation analysis of instantaneous HR versus 
time axis. HRV is sometimes called the R-R interval 
(RRI) analysis, where R is the peak point of the QRS 
complex in the ECG wave, or the Inter-Beat-Interval 
(IBI) analysis. When the individual is at rest, high 
HRV is favorable while low HRV is observed at an 
active or stressed state. HRV has been used as a 
measurement to assess overall cardiac health and 
reflect the state of the Autonomic Nervous System 
(ANS) activities (Hamilton G. et al. 2019). 

The ANS is the involuntary division of the 
nervous system and consists of autonomic neurons 
that conduct impulses from the central nervous system 
(brain and/or spinal cord) to glands, smooth muscles 
and cardiac muscles (DanTest Clinicians Team 2016). 
The role of the ANS is to continuously fine-tune the 
functions of organs and organs systems to maintain 
internal stability and balance. ANS has two main 
components called the Sympathetic and 
Parasympathetic Nervous Systems (SNS and PSNS 
respectively). The SNS triggers the fight or flight 
response leading to increased heart rate, blood 
pressure and sweating, and pupil dilation etc. On the 
other hand, the PNS  complements  the  operations  

(a) 

(b) 
Figure 1: (a) Normal ECG signal. 

(b) ECG signal at Apnea Episode.



Sarah Qasim Ali and Abdulnasir Hossen 

26 

performed by the SNS and triggers the rest and digest 
response where the opposite behavior occurs. When 
the airway is partially or completely obstructed; the 
heart rate changes and hence ECG signal alters. When 
the oxygen level decreases in the body during sleep 
apnea event the heart cells receives less oxygen and 
hence the heart rate is reduced and the R-R interval 
increases between consecutive beats as shown in 
Fig.1 (b) (Sharma S. et al. 2019).  

This alters the brain's sleep and wakes the brain 
for immediate action. The brain responds by sending 
strong tones to the respiratory system to increase 
breathing speed. The later increases the heart rate 
suddenly and hence increases the blood pressure in 
order to pump more blood to compensate for the lack 
of oxygen.  

In frequency-domain analysis, signals can be 
represented in a graph that shows how much (energy) 
of the signal lies within given frequency bands over a 
range of frequencies. The well-known Fourier 
methods such as the Fast Fourier Transform (FFT) 
implementation of the Discrete Fourier Transform 
(DFT) are usually used for identifying the available 
spectral content for both stationary and non-stationary 
signals (Polikar R 2011). However, for non-stationary 
signals, the frequency content varies with time and 
hence DFT based methods fail to provide the time-
related information at which those frequencies occur. 
Moreover, it can only reflect the frequencies that are 
present in the signal but not when they were present. 
Since most of the physiological signals like the ECG, 
etc. are non-stationary signals; time-frequency 
analysis such as the Short-Time Fourier Transform 
(STFT) and Discrete Wavelet Transform (DWT) are 
used as alternatives to the Fourier analysis when 
estimating the available PSD content (Polikar R 
2011). The classification features used in this research 
depend on the Power Spectral Density (PSD) at 
different frequency levels estimated by implementing 
the Discrete Wavelet Packet Decomposition (DWPD) 
method. This is to overcome the resolution related 
problems of the STFT. The discrete wavelet 
decomposition utilizes various mother wavelets of 
different scales to be able to adapt to fast and slow 
changes in the analyzed signal (Polikar R 2011). The 
wavelet decomposition method is implemented using 
filter banks (Misiti M et al. 1996). A set of high pass 
and low pass filters allow the signal to be decomposed 
reaching a certain decomposition level in which the 
signal can be further analyzed, de-noised or 
compressed (Misiti M et al. 1996). The PSD 
calculation is done by mathematical modulation to the 
filters output coefficients (Sysel P et al. 2008). The 
DWT allows only the low-frequency components of 
the low pass filter to be analyzed to further levels as 
they are thought to be the ones that carry important 
information (Misiti M et al. 1996). However, DWPD 
allows both outcomes of the filters (low and high-
frequency) to be further decomposed. The later 
emphasizes the outliers, edges and transient signals 

which are crucial to tackle the OSA episodes. Hence, 
the DWPD leads to finding more desirable features 
for classification applications.  

In time domain analysis, a signal instance’s real 
values are visualized. A time domain graph shows 
how a signal changes with time. OSA episodes can be 
analyzed by observing the cyclic length variability of 
the HRV (a.k.a. RRI) signal. The term NN is used 
sometimes in place of the RR to emphasize that 
normal beats are being processed. There exist 
multiple well-established features that are normally 
used to analyze the beat-to-beat intervals (Mietus J et 
al. 2002). These features include:  the Root Mean 
Square of Successive Differences (RMSSD), the 
Standard Deviation of Successive Differences 
(SDSD), the Standard Deviation (Std.) of entire RRI 
signal, the mean of the entire RRI signal, the NNx 
family measures which include (NN of x≤50: NN50, 
NN30, NN20…etc.), and finally the pNNx family 
measure. 

2. ECG DATABASE

It has been suggested by some researchers, that the 
uniqueness of the data sets affects the classification 
results of the different proposed methods, hence 
similar results cannot be obtained using different 
datasets (Lado M et al. 2011). In this research the 
ECG signals were collected from the Sleep 
Laboratory of the Physiology Department of the 
Sultan Qaboos University Hospital (SQUH) while 
performing PSG studies for 80 subjects. These 
records were obtained from 20 normal subjects 
(0<AHI<5), 20 mild subjects (5<AHI<15), 20 
moderate subjects (15<AHI<30) and 20 severe 
subjects (AHI≥30). The records were divided into two 
groups: a training set and a test set; where both of the 
sets comprise of 10 normal ECG signals and 10 
Apneic ECG signals from each of the mentioned 
groups (total of 40 signals per set). Features were 
extracted from both sets and the training set is used to 
train the neural networks for classification purpose 
while the test set was used to check the classification 
performance. 

3. RRI EXTRACTION

The HRV signal is generated by finding the R-to-R 
Intervals (RRI) from the original ECG signals as 
declared in Fig. 1 (a). The proposed method in (Al 
Ghunaimi B 2003) was used to generate the RRI data. 
In order to accurately identify those R peaks, QRS 
detection is to be carried. The QRS detector that was 
used in (Al Ghunaimi B 2003) is a part of the 
Physionet tools available in the Physionet website and 
is based on the Pan-Tompkins Algorithm. Intervals 
corresponding to Normal-to-Normal peaks are 
extracted. The generated RRI data could contain false 
intervals, missed intervals and/or ectopic intervals. 



Identification of Obstructive Sleep Apnea Using Artificial Neural Networks and Wavelet Packet Decomposition 

27 

False RR intervals were removed by setting the lower 
and upper limits of its values to ones found in normal 
subjects which are typically around 400-2000 
milliseconds. Removing of outliers was achieved 
using a 41-points Moving Average Filter (MAF). Re-
sampling at 1 Hz and substituting of missed peaks 
were then achieved by simple linear interpolation 
implemented by MATLAB (Al Ghunaimi B 2003). 
Re-sampling and estimation of missed value are 
intended to generate an equally spaced RRI data and 
preserve the temporal sequence that is necessary for 
the frequency domain analysis. At this point it can be 
assumed that a clean RRI data sampled at 1 Hz 
containing no missed or outlier values was generated. 

4. SPECTRAL FEATURES

In this research, the RRI signals were decomposed 
using discrete wavelet packet decomposition up to 9 
levels in order to define the VLF band precisely 
according to the standard definition (VLF starts at 
0.0033 Hz). At the ninth level, the signal would have 
been decomposed into 512 frequency bands including 
the low-frequency and high-frequency bands. 

The PSD summation of these bands provides the 
total power spectrum of the signal, while the PSD 
summation by grouping according to certain 
frequency bands would provide us the desired features 
for the analysis. The discrete RRI signals are sampled 
at Fs=1 Hz and the maximum spectral frequency of 
RRI is found by Fs/2=0.5 Hz. Using DWPD, the 0.5 
Hz is decomposed into 512 bands (9 levels = 29), 
while each band covers 0.5/512= 0.0009765625 Hz. 
Therefore, the standard spectral bands of the 
decomposed RRI are covered as shown in Fig. 2. In 
addition, the spectral values near zero Hz have the 
most energy content that dominates other spectral 
values. Therefore, we intend to exclude those spectral 
values below 0.0033 Hz; hence, the first three bands 
of the decomposed RRI signal were ignored and the 
PSD of the VLF was estimated starting from the 4th-
band at 0.00390625 Hz up to 0.040039063 Hz. 

Fig. 3 shows the spectral powers summation of the 
different bands defining each feature. Furthermore, 
three other features extracted from the power ratios of 
the VLF, LF and HF features are calculated to form a 
total of six spectral features. These ratios are 
described as in Fig. 4. 

In Fig. 5, the power of the HF band is sketched for 
both normal and severe subjects for the training set. 
The values of the HF band at normal subjects are 
higher than those of severe subjects. However, some 
severe subjects have powers that overlap with normal 
subjects resulting in difficulties to classify and hence 
a combination of features are to be investigated (The 
University of Nottingham 2017). 

5. STATISTICAL FEATURES

In this work, multiple features were calculated from 
the RRI signals in time domain using MATLAB 
software. These features include the Root Mean 
Square of Successive Differences (RMSSD) which is 
calculated by finding the square root of the mean of 
the successive differences between adjacent NNs. The 
Standard Deviation of Successive Differences 
(SDSD) where the Standard Deviation of Average NN 
Intervals (SDANN) is calculated over a short period 
of 5-minutes or so to measures the change in heart 
rate due to cycles longer than five minutes. The 
Standard Deviation (Std.) of entire RRI signal, the 
mean of the entire RRI signal, The NNx family 
measures which include (NN of x≤50: NN50, NN30, 
NN20 etc.), where it represents the number of pairs of 
successive NN’s that differ by more than x=50, 30, 
20…etc. milliseconds and finally the pNNx family 
measure which includes the NNx measure divided by 
the total number of NNs of the signal. To sum up, the 
features are: the RMSSD, pNN15, pNN20, pNN30, 
pNN50, NN15, NN20, NN30, NN50, SDANN and 
Standard deviation of the entire signal which equate 
to eleven features. 

In order to observe a relation between time 
domain and frequency domain features, the values of 
RMSSD, NN50, and pNN50 features were 
normalized by dividing them to the total power of the 
RRI signal as shown in Fig. 6. Similar behavior of 
Fig. 5 was observed where the power of the high-
frequency band for severe apnea level subjects 
decrease and the same features’ values increase for 
normal subjects. This allows us to use these features 
in a similar analogy as spectral features for 
representing the parasympathetic activity of the 
Autonomous Nervous System for instance. Of course, 
variations are also present in some of the OSA 
patients where their spectral or time domain features 
exhibit similar behavior as normal subjects making it 
more difficult to differentiate between the cases using 
a single feature. 

6. NEURAL NETWORKS

The Artificial Neural Networks (ANNs) were 
designed based on the rudimentary understanding of 
the biological nervous system back in the 1950s to 
help solving and computing any arithmetic or logical 
statement (Hagan M et al. 1996). The ANNs are a 
collection of computational units called neurons 
composed of inputs with weights, biases and transfer 
functions to perform the thresholding act and produce 
an output. The three networks used in this work are of 
supervised learning type; this is because we already 
have information about the subject’s original 
condition (normal, mild, moderate, and severe). In 
supervised learning, the training set consists of



Sarah Qasim Ali and Abdulnasir Hossen 

28 

Figure 2.  Frequency bands of RRI packet decomposition into 9 levels. 

Figure 3. Calculation of the three main features. 

Figure 4. Main three spectral ratio features. 

Figure 5.  Power spectral destiny (HF band) between normal
and sever subjects. Figure 6. Normalized pNN50 % values for normal and

severe subjects. 



Identification of Obstructive Sleep Apnea Using Artificial Neural Networks and Wavelet Packet Decomposition 

29 

100.(%) 
FNTNFPTP

TNTP
Accuracy






100.(%) 
FNTP

TP
ySensitivit




100.(%) 
FPTN

TN
ySpecificit




multiple training examples each is a pair of input 
(feature) and the desired output (target). The 
supervised learning algorithm then tries to process 
and analyze the training examples to produce an 
inferred function (classification boundary region), 
which can be used to map new examples. If the 
network training and the learning algorithm are 
performed well, an optimal scenario would be 
generated in which the network will generalize and be 
able to determine the class label (target) of unseen 
instances correctly (Mohri M et al. 2012). This 
section introduces the three artificial neural networks’ 
models used in this research and describes their 
learning algorithms and transfer functions briefly.  

The perceptron network refers to multiple-inputs 
single-neuron network. It is considered the building 
block of a more complicated network called the Feed 
forward Neural Network. Usually the single-layer 
perceptron is used for binary and linear classification. 
The feed forward network is one of the most 
commonly used artificial neural networks and is 
typically composed of multiple perceptrons aligned in 
layers. It is considered the first type of ANN’s that 
was used to solve non-linear problems where the 
perceptron has failed and is considered powerful 
networks that can almost approximate any function 
(Hagan M et al. 1996). Probabilistic Neural Networks 
(PNN’s) are widely used classification and pattern 
recognition. When employed for classification 
problems, the class probability of input is estimated 
and the class with the highest probability is selected 
as the output class. This means that the input belongs 
to the class that provides the highest probability when 
introduced to that input. The design of a PNN is 
straightforward and extremely less complicated than 
other multilayer networks. This is because it does not 
depend on weights learning and hence does not 
require training. A PNN network generalizes well and 
is guaranteed to converge to a Bayesian classifier 
(simple well-studied conditional probabilistic 
classifier) providing the correct probability when 
presented with enough training data samples. A PNN 
consists of several sub-networks, each of which is a 
Parzen window pdf estimator for each of the classes. 
The inputs are the set of measurements/features and 
are used as centers for the radial basis (Gaussian 
functions) of the second layer. The third layer 
performs an average operation of the outputs from the 
second layer for each class and a final voting is 
performed by the third layer selecting the largest 
value to determine the associated class label. 

In this research, the perceptron network is 
composed of one neuron of multiple inputs for one 
output classification, and two neurons for four outputs 
classification embedded with hard limit transfer 
function. The feedforward network is composed of 
two hidden layers each of five neurons (total 10) of 
the tangent-sigmoid transfer function and an output 
layer of one or two neurons according to the number 
of outputs (similar to perceptron layer) with pure 

linear transfer function. The probabilistic network is 
composed of input neurons changing according to the 
number of inputs with radial basis transfer function. 
Figs. 7 and 8 show the connections of the layers for 
the feed forward and probabilistic neural networks.      

The performance of the training step and testing 
step of the neural networks are investigated by 
calculating well-known performance metrics such as 
the specificity, sensitivity and accuracy. The 
specificity reflects the number of accurately 
diagnosed healthy subjects while the sensitivity 
reflects the number of the accurately identified 
patients. The accuracy is a measure of both of the 
correctly classified patients and normal among the 
total experiment set. In Fig. 9, the actual meaning for 
each performance metric can be observed while the 
following equations reveal how they are calculated. 

Figure 7.  Feedforward neural network layer connections. 

Figure 8.  Probabilistic neural network layer connections. 

(1) 

(2) 

(3)



Sarah Qasim Ali and Abdulnasir Hossen 

30 

Figure 9.  Networks performance metrics.

7. IMPLEMENTATION

In this work, four different versions of classification 
have been carried out using the perceptron network, 
the feedforward with back-propagation network and 
the probabilistic neural network. These classification 
versions include:  
•     Version 1: Normal vs. Severe Classification
•     Version 2: Normal vs. Mild Classification
•   Version 3: Normal vs. Patient (Mild, Moderate,
Severe) Classification 
•   Version 4: Normal vs. Mild vs. Moderate vs.
Severe Classification. 

The discrete wavelet packet decomposition was 
implemented to extract power estimations of the RRI 
signals of SQUH ECG database at the classical 
frequency bands with Bi-orthogonal mother wavelet 
at 9 levels decomposition. Four different schemes of 
training and test data are selected:  
 Scheme 1: Original first half data for trial:

Original second half data for the test.  
 Scheme 2: Original second half data for trial:

Original first half data for the test. 
 Scheme 3: Large set simulated from first-half data

for trial: Original second half data for test. 
 Scheme 4: Large set simulated from second-half

data for trial: Original first half data for test. 
The networks in scheme 1 and 2 are trained by the 

features of original data sets containing 10 subjects 
for each of the normal, mild, moderate and severe 
OSA states. While in scheme 3 and 4, the networks 
are trained using a large simulated set generated from 
the original data sets. The large training set was 
acquired by generating randomly, 1000 uniformly 
distributed feature sets between the maximum and 
minimum values of each feature from the original 
extracted spectral features. The networks testing and 
performance computation were carried by the 
remaining equivalent-size original data set of 10 
subjects in each case. 

The statistical time-domain features are used with 
classification version number 4 only as it is the most 
complicated classification version and hence 
investigation with different features may increase the 
chance of enhancing the performance.  

8. RESULTS AND DISCUSSION

Figures 10 and 11 display the actual classification 
accuracies of every network performing version-1 
classification of normal vs. severe OSA conditions 
and version-3 normal vs. patient OSA conditions 
respectively. The spectral features used are numbered 
in Figs. 11 and 12 as: (1. VLF, 2. LF, 3. HF, 4. 
VLF/LF, 5. VLF/HF, 6. LF/HF). The patient-class 
refers to a set including all the apneic levels of mild, 
moderate and severe subjects where the AHI index is 
exceeding 6 and up to 100 or more. The features were 
selected based on the highest training results they 
provided before testing the networks as well as for 
their lowest discrepancy among other schemes.  

Figures 12 and 13 summarize the results of the 
networks at versions-2 and version-4 classification by 
emphasizing the spectral features generating the 
highest performances. It can be noticed that version-4 
classification was the most difficult and the results are 
extremely poor. Herein, it can be concluded that 
spectral features alone are not enough for this 
complicated task. However, when using statistical 
time-domain features with the feedforward network; 
the result is extremely improved reaching an accuracy 
of 77.5% using two features only. The features are of 
pNN20 & pNN30 respectively. This shows that the 
statistical features extracted from the RRI signal at the 
time domain used with the feedforward network were 
able to generate a non-linear decision surface that 
helped classifying those subjects unlike the 
probability estimates or linear classification 
boundaries generated by the probabilistic and 
perceptron networks respectively. It can be observed 
that the feedforward with back-propagation neural 
network achieved high accuracies at different 
classification versions demonstrating the power and 
consistency of the network. Further, the specificity 
and sensitivity percentages are very close to each 
other at the different classification versions of the 
different networks which implies that only few unique 
cases were miss classified. Table 1, summarizes the 
results of the networks at the different classification 
versions by emphasizing the features generating the 
highest performances using scheme 1 of training. The 
classification performance between normal 
individuals and severe OSA patients was highest 
using the probabilistic network where the network 
was able to classify the subjects into either normal or 
severe conditions at 95% accuracy. This is an 
interesting result and shows how powerful the 
probabilistic network can be even when trained with a 
few sample points; it was still able to generalize well. 
Moreover, this accuracy was achieved when using a 
single spectral feature which is the very low 
frequency power feature. The test set included 20 
subjects (10 normal and 10 severe) meaning that the 
network only missed the correct classification of one 
severe subject at a sensitivity of 90%. 



Identification of Obstructive Sleep Apnea Using Artificial Neural Networks and Wavelet Packet Decomposition 

31 

Figure 10.   Networks Classification Results for Normal vs. 
Severe. 

Figure 11. Networks classification results for normal vs. 
patient. 

Figure 12. Networks classification results for normal vs. 
mild. 

Figure 13. Networks classification results for normal 
vs. mild vs. moderate vs. severe. 

Figure 14. Neural Networks Topology for the Four 
Classification Versions. 

The second classification version between normal 
and mild OSA patients witnessed its best performance 
at 95% accuracy by the feedforward network among 
all training schemes (original and large training sets). 
Hence, it can be considered a robust network 
algorithm, since many examples were introduced to 
the network that included different types of apneic 
episodes exhibiting different cessation occurrences 
and durations. Moreover, when comparing this result 
to other researches available in the literature, it is 
noticed that many of them had their highest results 
when dealing with small sets (~ 30 subjects where



Sarah Qasim Ali and Abdulnasir Hossen 

32 

~10 are normal and ~20 are severe). The latter makes 
our results acceptable and the employed database was 
efficient when used for classification. The third 
classification version between normal and OSA 
patients on the other hand, achieved performance 
accuracy of 87.5% using three different features 
(VLF+VLF/HF +LF/HF) by the feedforward network 
and achieved 85% accuracy by the perceptron 
network with the three different features (LF+ HF+ 
VLF/HF). Finally, it can be observed that the highest 
achieved test set accuracy of 77.5% in the fourth 
classification version between normal, mild, moderate 
and severe OSA subjects was accomplished by the 
feedforward network using a combination of two 
statistical features of pNN20 and pNN30 respectively.  
      The representations in Fig. 14 demonstrate each 
type of network topology with different features as 
inputs for the classification versions corresponding to 
the best accuracies listed in Table 1. 
For the third version, the perceptron network is 
sketched oxygenated blood although its performance 
accuracy was 85% and less than that of the 
Feedforward network 87.5%, in order to show the 
topology of the perceptron network. 

9. CONCLUSION

Obstructive sleep apnea (OSA) is a common 
breathing-related sleep disorder affecting individuals 
of different age groups, genders and origins. It is 
characterized by short-duration cessations in 
breathing during sleep due to the collapse of the upper 
airway. OSA is associated with major co-morbidities 
such as cardiovascular diseases, arrhythmias, strokes, 
obesity, depression, certain types of hypertension and 
diabetes. The golden and reliable standard test for the 
detection of OSA is a polysomnographic sleep study.

However, this test is time/labor-consuming, expensive 
and cumbersome. Analysis of a Heart Rate Variability 
(HRV) signal that is obtained from an 
Electrocardiograph (ECG) signal in time or frequency 
domain is an effective, non-invasive and promising 
method for the detection of OSA. 

In this research, single perceptron, feedforward 
with back propagation and probabilistic artificial 
neural networks are investigated for their performance 
in classifying SQUH database subject’s severity 
degree against four classification versions. The 
highest achieved accuracy of 95% was obtained when 
using VLF feature with the probabilistic neural 
network for normal vs. severe classification (version-
1). The feedforward neural network achieved an 
accuracy of 95% as well when classifying normal 
versus mild OSA patients at a combination of LF and 
VLF/HF ratio features. In Version 3, the feedforward 
network achieves 87.5% accuracy using three features 
VLF and VLF/HF and LF/HF for normal vs. patient 
(including: mild, moderate and severe subjects in one 
group) classification. In the same version, the 
perceptron network achieved the highest performance 
accuracy of 85% using LF along with HF and 
VLF/HF ratio combination of features. Finally, for 
OSA severity degree classification (verion-4) 
statistical time-domain features provided the highest 
accuracy of 77.5% when using a combination of 
pNN20 and pNN30 features with the feedforward 
neural network.  

The results are considered promising since the 
networks only used a maximum of three features to 
provide such results. Some of the limitations of this 
work include the ECG database size, neural networks 
training processing-time especially the feedforward 
network, feature dimensions (combinations of inputs). 
Future research recommendations are to be on 
investigating deep learning neural networks.

Network 
Type 

Classification  
Version 

Features Specificity Sensitivity Accuracy 

Probabilistic  Normal vs. Severe VLF 100% 90% 95% 

Feedforward  Normal vs. Mild LF+ VLF/HF 90% 100% 95% 

Perceptron 

Feedforward 

Normal vs. Patient 

Normal vs. Patients 

LF+ HF+VLF/HF 

VLF+VLF/HF +LF/HF 

80% 

90% 

86.67% 

86.67% 

85% 

87.5% 

Feedforward Normal vs. Mild vs. 
Moderate vs. Severe pNN20+pNN30 80% 76.67% 77.5% 

Table 1. Highest classification results of the networks at training scheme 1. 



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33 

CONFLICT OF INTEREST 

The authors declare no conflicts of interest. 

FUNDING

 No funding was received for this project. 

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