Detection of Obstructive Sleep Apnea Through ECG Signal Features

Abstract

Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.

Full text

Detection of Obstructive Sleep Apnea Through
ECG Signal Features
Laiali Almazaydeh, Khaled Elleithy, Miad Faezipour
Department of Computer Science and Engineering
University of Bridgeport
Bridgeport, CT 06604, USA
{lalmazay, elleithy, mfaezipo}@bridgeport.edu
Abstract—Obstructive sleep apnea (OSA) is a common disorder
in which individuals stop breathing during their sleep. Most of
sleep apnea cases are currently undiagnosed because of expenses
and practicality limitations of overnight polysomnography (PSG)
at sleep labs, where an expert human observer is needed to work
over night. New techniques for sleep apnea classification are
being developed by bioengineers for most comfortable and timely
detection. In this paper, an automated classification algorithm is
presented which processes short duration epochs of the
electrocardiogram (ECG) data. The automated classification
algorithm is based on support vector machines (SVM) and has
been trained and tested on sleep apnea recordings from subjects
with and without OSA. The results show that our automated
classification system can recognize epochs of sleep disorders with
a high degree of accuracy, approximately 96.5%. Moreover, the
system we developed can be used as a basis for future
development of a tool for OSA screening.
Keywords: sleep apnea, PSG, ECG, RR interval, feature
extraction, SVM.
I. INTRODUCTION
A. Background
Over the average lifespan, humans sleep for about 1/3 of
their lives. Feeling terrible after a night without sleep is the
body’s way of reminding us that sleeping is a necessity such as
eating, drinking and breathing. As we sleep, our body repairs
itself. This rejuvenation goes for hormones and muscles as well
as neural responses, like memory. Without sleeping, we simply
do not function as well as we can.
A sleeping disorder is when one cannot sleep, causing the
body to lose function. Just as the body’s benefits of rest can
range from physical to emotional and psychological effects,
lack of sleep can damage the body physically, emotionally and
psychologically. Till date, 84 kinds of sleep disorders have
been discovered, including the most common ones such as
insomnia, sleep apnea, narcolepsy and restless leg syndrome
[1].
Sleep apnea (SA) is the instance when one either has pauses
of breathing in their sleep, or has very low breath while asleep.
This pause in breathing is known as an apnea, and can range in
frequency and duration. The lack of breathing during sleep is
known as a hypopnea [2].
Sleep apnea is classified into two different types. The first
type is Obstructive Sleep Apnea (OSA), which is more
common, occurring in 2% to 4% of middle-aged adults and 1%
to 3% of preschool children [3], and is generally caused by a
collapse of the upper respiratory airway. The second one is
Central Sleep Apnea (CSA), which is caused by an absent or
inhibited respiratory drive. Most cases of CSA are mixed,
meaning that it is often along with OSA cases, and the CSA
only cases appear exceedingly rarely [4].
Like all sleeping disorders, symptoms of sleep apnea do not
occur just during the night. Daytime symptoms can range from
excessive sleepiness, impaired concentration, depression, early
morning headaches, memory loss and irritability [3].
During the night, symptoms can include nocturnal choking,
heavy snoring, sweating, restless sleep, impotence, and
witnessed apnea. While (OSA) is not a rare condition, it is most
likely for its victims to be middle aged or elderly. Those
affected by OSA also tend to be obese [3]. Despite how
common it is, most cases go undiagnosed and can be attributed
to 70 billion dollars loss, 11.1 billion in damages and 980
deaths each year [5].
Most cases go undiagnosed because of the inconvenience,
expenses and unavailability of testing. Testing is inconvenient
to the patient because it requires them to spend the night away
from their bed causing discomfort. It is expensive because
testing is done in the hospital, causing machines and various
technicians and staff to work over night. Testing is also widely
unavailable due to sleep centers operating at full capacity and
those on the waiting list can be untreated for an additional 6
months.
The traditional testing process includes a polysomnography
(PSG), which is a standard procedure for all sleep disorder
diagnosis. It records the breath airflow, respiratory movement,
oxygen saturation, body position, electroencephalogram
(EEG), electrooculogram (EOG), electromyogram (EMG), and
electrocardiogram (ECG) [6].
To summarize, the mere dependency on PSG needs to be
taken away from the laboratory for simpler detection and faster
treatment. Instead, automated, at-home devices that patients
can simply use while asleep seem to be very attractive and
highly on-demand.
978-1-4673-0818-2/12/$31.00 ©2012 Crown

B. Contribution
Though PSG has been widely used to detect sleep apnea,
many other techniques that rely on one biometric (e.g. ECG)
have been thoroughly investigated. To identify sleep apnea,
we propose a novel methodology in this paper that combines
RR-interval based features of the ECG signal based on the two
approaches suggested by Chazal et al., and Yilmaz et al.
Performance assessment of the combination of these two
approaches is done by measuring the classification
performance in determining the presence of apnea for different
epoch lengths.
C. Paper Organization
The rest of this paper is organized as follows. In Section II,
we glance at a variety of sleep apnea detection methods.
Section III contains an overview of the system, including a
description of the database of subjects, and details on the
analysis methodology of the paper. We describe the steps to
determine RR-interval and features extraction for different
epoch lengths in the same Section. A detailed clarification on
the SVM classifier used in our system is also provided. In
Section IV, we detail the results of our system, and then we
present a comparison with other SA detection works. Finally,
Section V concludes this paper regarding the potential
usefulness of our system, and highlights some directions for
future research.
II. R
ELATED WORK
Several methods have been suggested for identification of
sleep apnea over the past few years. Statistical features of
different signals such as nasal air flow, the thorax and abdomen
effort signals, acoustic speech signal, oxygen saturation,
electrical activity of the brain (EEG), and electrical activity of
the heart (ECG) are commonly used in the detection.
Ng et al. [7] showed that thoracic and the abdominal signals
were good parameters for the identification of the occurrence
of sleep apnea. Using the mean of absolute amplitudes of the
thoracic and the abdominal signals, they have achieved a good
performance with a receiver operating characteristic value
higher than 80%.
Depending on the hypothesis that speech signal properties
of OSA patients will be different than those not having OSA,
Goldshtein et al. [8] developed a gaussian mixture model-based
system to classify between the OSA and non-OSA groups.
They achieved a specificity and sensitivity of 83% and 79% for
the male OSA and 86% and 84% for the female OSA patients,
respectively. Their technique relied on vocal tract length and
linear prediction coefficients features.
The study in [9] assessed analysis of a comprehensive
feature set based on blood oxygen saturation (SaO
2
) from
nocturnal oximetry in order to evaluate sleep quality. The three
features of SaO
2
signal which are delta index, central tendency
measure and oxygen desaturation index are evaluated. Central
tendency measure accuracy was higher than those provided by
delta index and oxygen desaturation index. With central
tendency measure the sensitivity was 90.1% and the specificity
was 82.9%.
The relationship between periodic changes in the oxygen
saturation (SaO
2) profile and in the EEG pattern due to apnea
events during the night was investigated in [10]. The spectral
analysis of these two signals achieved 91% sensitivity, 83.3%
specificity and 88.5% accuracy in OSA diagnosis.
In [11], the authors analyze various feature sets and a
combination of classifiers based on the arterial oxygen
saturation signal measured by pulse oximetry (SpO
2
) and the
ECG in order to evaluate sleep quality and detect apnea. With
selected features of the SpO
2 and ECG signals, the Bagging
with REP Tree classifier achieved sensitivity of 79.75%,
specificity of 85.89% and overall accuracy of 84.40%.
Wavelet transforms and an artificial neural network (ANN)
algorithm were applied to the EEG signal in [12] to find a
solution to the problem of identifying sleep apnea (SA)
episodes. The system's identification results achieved a
sensitivity of approximately 69.64% and a specificity of
approximately 44.44%.
Many studies show that detection of obstructive sleep
apnea can be performed through heart rate variability (HRV)
and the ECG signal.
Quiceno-Manrique et al. [13] proposed a simple diagnostic
tool for OSA with a high accuracy (up to 92.67%) using time-
frequency distributions and dynamic features in ECG signal.
Moreover, based on spectral components of heart rate
variability, frequency analysis was performed in [14] using
Fourier and Wavelet Transformation with appropriate
application of the Hilbert Transform, where the sensitivity was
90.8%.
In addition, in [15] a bivariate autoregressive model was
used to evaluate beat-by-beat power spectral density of HRV
and R peak area, where the classification results showed
accuracy higher than 85%.
The technique in this work also relies on features of the
ECG signal.
III. METHODOLOGY
In this work, we focus on the ECG signal features to detect
sleep apnea. The block diagram of the overall methodology
used in this study is shown in Figure 1.
A. Subjects
The database of ECG signals used is available from the
PhysioNet web site [16]. PhysioNet contains a growing
collection of biomedical signals from healthy subjects and
patients. The PhysioNet web site is a public service of the
PhysioNet Resource funded by the National Institutes of
Health's NIBIB and NIGMS.
PhysioNet offers free access to Apnea-ECG Database,
which we used to assess and validate our approach. The Apnea-
ECG Database contains 70 recordings, containing a single
ECG signal varying in length from slightly less than 7 hours to
nearly 10 hours each. The sampling frequency used for ECG
acquisition was 100 Hz, with 16-bit resolution, and one sample
bit representing 5µV. The standard sleep laboratory ECG
electrode positions were used (modified lead V2) [6].

Figure 1. Schematic diagram of the system.
The subjects of these recordings were men and women
between 27 and 63 years of age (mean: 43.8±10.8 years) with
weights between 53 and 135 kg (mean: 86.3±22.2 kg). The
sleep recordings originated from 32 subjects (25 men, 7
female) those who were recruited for previous studies on
healthy volunteers, and also patients with obstructive sleep
apnea [6].
B. ECG
The electrocardiogram is a representation of the electrical
activity of the heart; each activity has a distinctive waveform.
Normal ECG graph consists of the P wave, QRS complex and
the T wave. A small U wave is normally visible in 50-75% in
the ECG [17]. Figure 2 shows a schematic representation of
normal ECG.
ECG is considered as one of the most efficient features to
detect sleep disorders. Cyclic variations in the duration of a
heartbeat, also known as RR intervals (time interval from one
R wave to next R wave) of ECG have been reported to be
associated with sleep apnea episodes. This consists of
bradycardia during apnea followed by tachycardia upon its
cessation [6].
According to [17], RR interval time series is generated for
each ECG beat, as follows:
)1(.1,...,2,1),()1()( −=−+= niiririrr
RR-interval is defined as the time interval between two
consecutive R peaks.
Figure 2. Schematic representation of normal ECG.
Several researches have been conducted to recognize sleep
apnea using the features derived from the RR interval such as
median, mean, inter-quartile range (IQR), and the standard
deviation of the change in RR intervals [6][17][18].
C. Data Preparation
To select the data, we chose the ECG records which have
continuous apnea data for a certain period of time, followed by
a regular (normal) data representation for a period of time, or
vice versa. The data preparation is used for training the SVM
classifier (see subsection III.G).
To give more clarification about data preparation in our
work, an example is provided regarding the data selection. In
data set1 of record a03, to get regular data, we chose the data
from 2:27:00.000 to 2:57:00.000, and to get apnea data, we
chose the data from 3:06:00.000 to 3:36:00.000. The reason of
choosing those periods was because the data at those periods
have clear apnea and regular data.
In data set2 of record b02, to get apnea data, we chose
apnea data from 1:17:00.000 to 1:37:00.000, and to get regular
data, we chose regular data from 1:57:00.000 to 2:17:00.000.
Similarly, the reason of choosing those periods was because
the data at those periods had clear apnea and regular data, and
they are within the same hour.
MATLAB toolset was used in our experiments for signal
processing. The data records were imported as MATLAB
matrices (.mat) from the Physionet web site.
The next step in our procedure after data selection is data
partitioning. In our work, three cases of partitioning were
analyzed, as follows:
• Case 1. The apnea and regular data are partitioned
into 10 second pieces
• Case 2. The apnea and regular data are partitioned
into 15 second pieces
• Case 3. The apnea and regular data are partitioned
into epochs of 30 second pieces.
Since apnea is defined as a pause in breathing, and can last
from a few seconds to minutes (almost >=10 sec); we
investigate the three above cases to determine the best accuracy
that can be achieved.
ECG signal
Data Preparation
RR Interval Detection
Features Extraction
Data Randomization
SVM
Performance Evaluation

Figure 3. Detection of R- Peak.
D. RR Interval Detection
We need to distinguish the R waves from the other waves
of the ECG signal. Therefore, we developed the following two
conditions, in which R-peak was detected. An R peak will be
identified if both conditions 1 and 2 are satisfied:
1) It has to be a local maximum, which is detected by a
local max function within a window of 150ms.
2) The local max peaks must be at least 2 standard
deviation above the mean.
Once the R-peak was determined, RR intervals were
computed. The RR interval is the peak to peak time period
from two continuous peak signals as shown in Equation 1.
Figure 3 shows the detection of R-peaks.
E. Features Extraction
Our technique relies on an effective combination of ECG
signal features which is a novel hybrid of features extracted
from [6] and [19]. According to [17], the following ECG
features which are most effective for apnea detection are
calculated:
• Mean epoch and recording RR-interval.
• Standard deviation of the epoch and recording RR-
interval.
• The NN50 measure (variant 1), defined as the number
of pairs of adjacent RR- intervals where the first RR-
interval exceeds the second RR- interval by more
than 50 ms.
• The NN50 measure (variant 2), defined as the number
of pairs of adjacent RR-intervals where the second
RR-interval exceeds the first RR interval by more
than 50 ms.
• Two pNN50 measures, defined as each NN50
measure divided by the total number of RR-intervals.
• The SDSD measures, defined as the standard
deviation of the differences between adjacent RR-
intervals.
• The RMSSD measures, defined as the square root of
the mean of the sum of the squares of differences
between adjacent RR- intervals.
• Median of RR-intervals.
• Inter-quartile range, defined as difference between
75
th
and 25
th
percentiles of the RR-interval value
distribution.
• Mean absolute deviation values, defined as mean of
absolute values obtained by the subtraction of the
mean RR-interval values from all the RR-interval
values in an epoch.
The first seven features are proposed by Chazal et al. [6],
while the three latter features are proposed by Yilmaz et al.
[19], who claimed that RR interval mean, standard deviation,
and range are sensitive to outliers, and thus classification
performance deteriorates when only these features are
included.
Our hybrid technique includes a combination of the most
effective set of RR-interval based features of the ECG signal
for classification. The classification results confirm the
improved accuracy compared to the two above techniques.
F. Data Randomization
In this step, we separate the apnea and non apnea data. We
then separate training data and testing data, with 80% for the
training and 20% for the testing.
We use a MATLAB built-in function (rand) to determine
whether a feature set in 10s (or 15s or 30s) of data belongs to
test group or rule creation group. If ‘rand’ is larger than 0.2,
then the 10s data will belong to rule creation group, otherwise
it will belong to testing group.
After the signals are separated, we perform the training
for SVM.
G. Support Vector Machines
We use Support Vector Machines (SVMs) as a
classification (also known as supervised learning) method in
order to investigate apneaic epoch detection.
SVMs are learning methods, which aim to find the optimal
separating plane that analyze data and recognize pattern used
for regression analysis.
In SVM, P data is classified to which class it belongs, by
points with a (P − 1) dimensional hyperplane, which is called
a linear classifier. The optimal hyperplane that separates the
clusters of vectors is found by SVM modeling. The cases with
one category of the target variable are on one side of the plane
and cases with the other category are on the other side of the
plane. Figure 4 illustrates the working principle of SVM.
A good separation between the two possible classes is
achieved by building a maximal margin hyperplane. The
margin maximizes the distance between the classes and the
nearest data point of each class. In general, the larger the
margin is, the lower the generalization error of the classifier
[17]. Figure 5 shows the trade off margin choice.
In addition, SVMs handles the separation by a kernel
function to map the data into a different space with a
hyperplane. SVM gives the flexibility for the choice of the
kernel, as shown in Figure 6. Linear, polynomial and radial
can be taken as an example for a kernel function.

Figure 4. The SVM algorithm.
Figure 5. Trade off margin choice.
Figure 6. Kernal choice.
The choice of a kernel depends on the problem we are
trying to model. Polynomial kernels are well suited for
problems where all the training data is normalized, and it
allows to model feature conjunctions up to the order of the
polynomial. Radial basis functions allow picking out circles
(or hyperplanes). In contrast, the linear kernel, allows only
picking out lines (or hyperplanes) [20].
In our implementation, we use a linear kernel function to
map the training data into kernel space. In the optimization
process, we use a method called sequential minimal
optimization to find the separating hyperplane.
IV.
RESULTS
A. Performance Evaluation
We evaluated the effectiveness of our model on the Apnea-
ECG database, using different records available in that
database. The model was implemented using MATLAB
toolset.
To evaluate the performance of the classification system,
two statistical indicators, Sensitivity (Se) and Specificity (Sp)
in addition to the Accuracy (Acc) have been used. The
sensitivity of a test is the percentage of patients in the OSA
positive group correctly diagnosed, whereas the specificity is
the percentage of subjects in the OSA negative group correctly
classified by the test.
Table I, II and III show the classification results for the
three cases mentioned in the data partitioning step. Our model
was based on a linear kernel SVM using various RR-interval
features of the ECG signal. The three cases used here are:
(i)
10 seconds data partitioning, (ii) 15 seconds, and (iii) 30
seconds.
The accuracy of our approach is 86.1%, 96.5%, and 95%,
respectively.
From Table II, SVM with linear kernel using 15 second
epochs shows the best classification accuracy with high
successful rate of correct prediction.
TABLE I
10 sec. (Accuracy is 86.1%)
Input\Output Regular Apnea
Regular
97.2%
2.78%
Apnea 25%
75%
TABLE II
15 sec. (Accuracy is 96.5%)
Input\Output Regular Apnea
Regular
100%
0%
Apnea 7.1%
92.9%
TABLE III
30 sec. (Accuracy is 95%)
Input\Output Regular Apnea
Regular
100%
0%
Apnea 10%
90%
B. Comparison with other techniques
We performed a comparison with other SA detection
works. Table IV represents comparative results. As can be
seen, our system has achieved a comparable or better
performance. This applies to the other works that rely on the
ECG signal as well as other biometric signals.
Small Margin
Large Margin
Small Margin Large Margin
Support Vectors
Input Space Input Space
Features Space
Marg
i
n
Ma
pp
in
g
Solution

TABLE IV
Comparison of Sleep Apnea Detection Approaches
Method Ref. Approach
Perform
a
nce
[%]
S
e
S
p
Acc.
Chazal et al. [6] Measure of minutes of
sleep disordered
respiration
NA NA 91
Alvarez et al. [9] SaO2 90.1 82.9 NA
Alvarez et al. [10] SaO2 and EEG signal 91 83.3 88.5
Xie et al. [11] SpO2 and ECG 79.75 85.89 84.40
Lin et al. [12] EEG signal 69.64 44.44 NA
Quiceno-
Manrique et
al.
[13] ECG signal NA NA 92.67
Schrader et
al.
[14] Fourier and Wavelet
Transformation of HRV
90.8 NA NA
Mendez et al. [15] Bivariate autoregressive
model of HRV
NA NA 85
Yilmaz et al. [19] RR-interval based
classification
NA NA 89
Proposed - Features extraction of
ECG signal
92.9 100 96.5
V. CONCLUSIONS AND FUTURE WORKS
In this work, we studied the possibility of the detection of
sleep apnea or hypopnea events from the ECG signal variation
patterns during sleep. We further developed a model using the
ECG signal features and evaluated its effectiveness. Our model
was based on a selective set of RR-interval-based features that
were given to an SVM for classification. We evaluated our
model on three different epoch lengths.
From the experimental results, we conclude that SVM with
linear kernel shows the best accuracy with 15 second epoch
length.
As a future work, we plan to do performance optimization
for feature selection, and then incorporate this work into a real-
time monitoring system that acquires and analyzes the ECG
signal of subjects during sleep.
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