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Sleep apnea is the instance when one either has pauses of breathing in their sleep, or has very low breath while asleep. This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PSG) at sleep labs. PSG is both expensive and inconvenient as an expert human observer is required to work over night. New sleep apnea classification techniques are nowadays being developed by bioengineers for most comfortable and timely detection. This paper focuses on an automated classification algorithm which processes short duration epochs of the electrocardiogram (ECG) data. The presented classification technique 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 accuracy of 96.5% or higher. Furthermore, the proposed system can be used as a basis for future development of a tool for OSA screening.
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Obstructive Sleep Apnea Detection Using
SVM-Based Classification of 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}
Abstract Sleep apnea is the instance when one either has
pauses of breathing in their sleep, or has very low breath while
asleep. This pause in breathing can range in frequency and
duration. Obstructive sleep apnea (OSA) is the common form
of sleep apnea, which is currently tested through
polysomnography (PSG) at sleep labs. PSG is both expensive
and inconvenient as an expert human observer is required 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 classification
technique 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.
A. Background
Over the average lifespan, humans sleep for about 1/3 of
their lives. 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]. 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 sleep apnea cases go undiagnosed because of the
inconvenience, expenses and unavailability of testing. 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].
B. Contribution and Paper Organization
It is clear that the mere dependency on PSG needs to be
taken away from the laboratory for simpler detection and
faster treatment of sleep apnea. Instead, automated, at-home
devices that patients can simply use while asleep seem to be
very attractive and highly on-demand. We propose a novel
methodology in this paper that combines most effective RR-
interval based features of the ECG signal based on the ones
suggested by Chazal et al., and Yilmaz et al. This work
relies on SVM for classification. 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.
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, 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. In Section IV,
we present the results of our system, and then we provide 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
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
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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
) from
nocturnal oximetry in order to evaluate sleep quality. The
three features of SaO
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 (SaO2) 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
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
) and the
ECG in order to evaluate sleep quality and detect apnea.
With selected features of the SpO2 and ECG signals, the
Bagging with REP Tree classifier achieved sensitivity of
79.75%, specificity of 85.89% and overall accuracy of
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
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
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]. 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.
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]. RR-interval is defined as the time interval
between two consecutive R peaks. The RR interval time
series generated for each ECG beat can be written as follows
)1(1,...,2,1),()1()( niiririrr
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).
The next step in our procedure after data selection is data
partitioning. In our work, three cases of partitioning were
analyzed, as follows:
ECG signal
Data Preparation
RR Interval Detection
Features Extraction
Data Randomization
Performance Evaluation
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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.
Figure 2. 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
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 2 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]. 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-
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
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
and 25
percentiles of the RR-interval value
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
The first seven features are proposed by Chazal et al.
[6], while the three latter feature 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. Support Vector Machines
We use Support Vector Machines (SVM) as a
classification (also known as supervised learning) method in
order to investigate apneaic epoch detection. 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.
For data randomization, 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. After the
signals are separated, we perform the training for SVM.
A. Performance Evaluation
We evaluated the effectiveness of our model on the
different records in the Apnea-ECG database. MATLAB
toolset was used for signal processing and classification.
Two statistical indicators, Sensitivity (Se) and
Specificity (Sp) in addition to the Accuracy (Acc) have been
used to evaluate the performance of our classification
system. 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:
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
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Preprint submitted to 34th Annual International IEEE EMBS Conference.
Received March 29, 2012.
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
Q-Manrique et
al. [13]
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. 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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CONFIDENTIAL. Limited circulation. For review only.
Preprint submitted to 34th Annual International IEEE EMBS Conference.
Received March 29, 2012.
... The signals were obtained directly by electrodes and then amplified. The PSG signalswere scored offline by sleep experts [7]. These signals contain of: Electromyogram (EMG); Electrooculogram (EOG), Electrocardiogram (ECG) and Electroencephalogram (EEG) [7]. ...
... The PSG signalswere scored offline by sleep experts [7]. These signals contain of: Electromyogram (EMG); Electrooculogram (EOG), Electrocardiogram (ECG) and Electroencephalogram (EEG) [7]. ...
... Features extracted ECG provide an efficient means of detecting sleep disorders. [7] used the RR-Interval of the ECG signals as features inputted. The results showed that system detected epochs of apnea with an accuracy of 96.5%. ...
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Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep apnea leads to fatal complications in both psychological and physiological being of human. Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were computed, and features were extracted from these domains. These features are inputted into two machine learning algorithms: Support Vector Machine and K-Nearest Neighbors of different kernel functions and orders. Evaluation metrics such as specificity, accuracy, and sensitivity are computed and analyzed for the classifiers. The KNN classifier outperforms the SVM in classifying apnea from non-apnea events in patients. The KNN order 3 shows the highest performance sensitivity of 85.92%, specificity of 80% and accuracy of 82.69%.
... Recent studies have revealed that 4 percent of men and 2 percent of women over 50 years suffer from OSA [13,34]. Additionally, 2-4 percent of middle-aged adults and 1-3 percent of children are also suffering from OSA [2]. One of the standard methods for diagnosis of the OSA is to record various biological signals during the sleep. ...
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... Over the next few years, several studies used neural network models to score sleep studies in patients with obstructive sleep apnea (OSA), epilepsy, Cheyne Stokes respirations, and Parkinson's disease [24][25][26][27][28]. While some of the studies focused on analyzing sleep spindles and power spectra of sleep for staging, others focused on integrating cardiorespiratory events to diagnose sleep-related breathing disorders while a few focused on snore signal [29][30][31][32][33][34]. ...
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... Most of the current research on sleep apnoea focuses on comprehensive portable devices to offline systems for automated detection through the analysis of different signals stored on PSG [11][12][13][14][15]. Time-and frequency-domain features of electromyography (EMG) signals, the thorax and abdomen effort signals, acoustic speech signal, oxygen saturation (SpO2), electroencephalography (EEG) and electrocardiography (ECG) signals have been used in the literature for detection of sleep apnoea [16,17]. ...
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... However, the procedure of polysomnography includes multiple sensors, causing discomfort and resulting in a non-homecare friendly experience for the patients [7]. Studies have stated that these are some of the main factors of underdiagnosed statistics and highly untreated patients of sleep apnea [8]. Thus, the urge for a simpler and more comfortable alternative to promote apnea detection in daily life has encouraged studies in this field [9]. ...
... In some cases, this is not significant enough compared to other normal 60-80 beat ECGs. Cutting the duration of the ECG signal to a shorter length is likely to improve accuracy, although it will increase the difficulty in classification and data collection [28]. ...
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... Several studies report implementation of machine learning and deep learning techniques to automate the apnea-hypopnea detection process from PSG signals [9][10][11][12], but the previously noted limitations persist. ...
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Objective Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea–Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO2 signals using a large (n = 412) dataset serving as ground truth.DesignSimultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%) feature, one allowing a time lag of 30 s between the two signals.ResultsEvent-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO2, respiration-only, and SpO2-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively.ConclusionsA wearable respiratory effort signal with or without SpO2 signal predicted AHI accurately, and best performance was achieved with using both signals.
Objective: Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. Approach: In this study, a hierarchy of HMMs named Layered HMM was presented to detect respiratory events from PSG recordings. The recordings of 210 PSGs from Massachusetts General Hospital's database were used for this study. To develop detection algorithms, extracted feature signals from airflow, movements over the chest and abdomen, and oxygen saturation in blood (SaO2) were chosen as observations. The respiratory disturbance index (RDI) was estimated as the number of apneas, hypopneas, and RERAs per hour of sleep. Main results: The best F1 score of the event by event detection algorithm was between 0.22±0.16 and 0.70±0.08 for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived RDI (R2=0.91, p<0.0001). The best recall of RERA detection was achieved 0.45±0.27. Significance: The results showed that the layered structure can improve the performance of the detection of respiratory events during sleep.
PurposeSleep apnea causes heart rate variability (HRV). HRV can be detected from the electrocardiography (ECG) signal and descriptors of HRV during sleep have been shown to be useful predictors of sleep apnea. In this work, we study the use of raw ECG signal and deep one-dimensional residual neural network (1-D ResNet) for end-to-end sleep apnea detection.Methods Our method uses raw single-lead ECG signal as an input to a 1-D convolutional neural network (CNN) with residual connections, exploiting CNN’s ability to learn distinguishing signal characteristics directly from the ECG signal and thereby forgoing the need for human engineered signal processing, feature extraction, and feature selection. In addition, we use weighted cross-entropy loss to account for the imbalance of apnea and non-apnea segments in our dataset, Bayesian optimization for fine-tuning the network hyperparameters, and data from current and adjacent epochs for predicting the label of the current epoch. The final ECG-based apnea detection network is evaluated on a dataset of 70 overnight ECG recordings.ResultsThe proposed method achieved an accuracy of 93.05% (AUC = 0.9819) in detecting sleep apnea segments when considering adjacent epochs, thus, outperforming several baseline techniques. Furthermore, the method achieved 100% accuracy in separating sleep apnea recordings from normal recordings.Conclusion Our simple yet robust approach to ECG-based apnea detection demonstrates high accuracy. It has the potential to improve detection and diagnosis of sleep apnea and improve quality of life and health outcomes for millions of people worldwide.
Conference Paper
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The purpose of this study is to find optimal features and classifier's model selection for sleep apnea detection using ECG signals. We want to determine whether a set of unknown ECG signals (test data) is from heavy apnea, mild apnea, or healthy categories. We examine two recent approaches of features selection: an approach proposed by Chazal et al. (2004), which is based on the RR-interval mean and time-series analysis; and an approach proposed by Yilmaz et al. (2010), which is based on the RR-interval median. We also examine cross validation and random sampling method in the classifier's probability model selection. We evaluate the approaches using three classifiers: k-Nearest Neighbor (kNN), Naive-Bayes and Support Vector Machine (SVM). In addition, we use a self organizing map (SOM) clustering or preprocessing to provide better sample that can represent the entire training data. Our experiment using ECG data from PhysioNet shows that classification results using only 3 features as proposed by Yilmaz et al. (2010) gives about 3.59% gain on overall classification accuracy (CA) and 7.5% gain on area under ROC-curve (AUC) on than the classification accuracy using 8 features as proposed by Chazal et al., (2004).
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This paper describes implementation of Principal Component Analysis (PCA) on sleep apnea detection using Electrocardiogram (ECG) signal. The statistics of RR-intervals per epoch with 1 minute duration were used as an input. The combination of features proposed by Chazal and Yilmaz was transformed into orthogonal features using PCA. Cross validation, random sampling, and test on train data were used on model selection. The results of classification using kNN, Naïve- Bayes, and Support Vector Machine (SVM) show that PCA features give better classification accuracy compared to Chazal and Yilmaz features. SVM with RBF (Radial Basis Function) kernel gives the best classification accuracy by using 7 principal components (PC) as a features. The experimental results show that relation between Chazal features with target class tend to be linear, but Yilmaz and PCA features are non-linear.
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This paper describes a new technique to classify and analyze the electroencephalogram (EEG) signal and recognize the EEG signal characteristics of Sleep Apnea Syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN). The EEG signals are separated into Delta, Theta, Alpha, and Beta spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. We treated the wavelet coefficient as the kind of the training input of artificial neural network, might result in 6 groups of wavelet coefficients per second signal by way of characteristic part processing technique of the artificial neural network designed by our group, we carried out the task of training and recognition of SAS symptoms. Then the neural network was configured to give three outputs to signify the SAS situation of the patient. The recognition threshold for all test signals turned out to have a sensitivity level of approximately 69.64 and a specificity value of approximately 44.44 . In neurology clinics, this study offers a clinical reference value for identifying SAS, and could reduce diagnosis time and improve medical service efficiency.
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Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties. We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following analysis of the study, subjects were divided into OSA (n=67) and non-OSA (n=26) groups. A Gaussian mixture model-based system was developed to model and classify between the groups; discriminative features such as vocal tract length and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79% were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a system can be used as a basis for future development of a tool for OSA screening.
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Polysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal. For this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed. QDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM. This study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.
Conference Paper
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Detection of obstructive sleep apnea can be performed through heart rate variability analysis, since fluctuations of oxygen saturation in blood cause variations in the heart rate. Such variations in heart rate can be assessed by means of time-frequency analysis implemented with time-frequency distributions belonging to Cohen's class. In this work, dynamic features are extracted from time frequency distributions in order to detect obstructive sleep apnea from ECG signals recorded during sleep. Furthermore, it is applied a methodology to measure the relevance of each dynamic feature, before the implementation of k-nn classifier used to recognize the normal and pathologic signals. As a result, the proposed method can be applied as a simple diagnostic tool for OSA with a high accuracy (up to 92.67%) in one-minute intervals.
To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO (2) features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO(2) and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.
This study assessed the hypothesis that blood oxygen saturation (SaO(2)) and electroencephalogram (EEG) recordings could provide complementary information in the diagnosis of the obstructive sleep apnea (OSA) syndrome. We studied 148 patients suspected of suffering from OSA. Classical spectral parameters based on the relative power in specified frequency bands (A(f-band)) or peak amplitudes (PA) were used to characterize the frequency content of SaO(2) and EEG recordings. Additionally, the median frequency (MF) and the spectral entropy (SE) were applied to obtain further spectral information. We applied a forward stepwise logistic regression (LR) procedure with crossvalidation leave-one-out to obtain the optimum spectral feature set. Two features from the oximetric spectral analysis (PA and MFsat) and three features from the EEG spectral analysis (A(delta), A(alpha) and SEeeg) were automatically selected. 91.0% sensitivity, 83.3% specificity and 88.5% accuracy were obtained. These results suggest that MF and SE could provide additional information to classical frequency characteristics commonly used in OSA diagnosis. Additionally, nocturnal SaO(2) and EEG recordings during the whole night could provide complementary information to help in the detection of OSA syndrome.
To evaluate the sensitivity of mean absolute amplitudes of the thoracic and the abdominal signals as a prompt indicator of the occurrence of sleep apnoea events. To provide symptomatic management of sleep apnoea, a reliable method of detecting sleep apnoea is essential to ensure that the intervention can be applied only when needed. It is also crucial to identify the threshold for the trigger of an intervention using a deployed sensor. Twenty-six subjects aged between 18-65 years who were diagnosed with obstructive or central sleep apnoea underwent an overnight sleep study. Signals of nasal and oral airflow, thoracic and abdominal efforts and pulse oximetry level were recorded using a polysomnography device. With a 95% CI, the overall area under the receiver operating characteristic of the thoracic signal, the abdominal signal and the combination of the thoracic and the abdominal signals were 84.56, 87.48 and 90.91%, respectively. Using -20, -25 and -30% as a cut-off point, the sensitivity values of thoracic signal, abdominal signal and combination of the thoracic and the abdominal signals ranged from 70.29-86.25% and the specificity values ranged from 74.82 to 90.09%. Using mean absolute amplitude analysis, the results of this study showed that combination of the thoracic and the abdominal signals achieved the best overall and individual performances compared with thoracic signal and abdominal signal. Overall, thoracic signal, abdominal signal and combination of the thoracic and the abdominal signals have a good performance with an receiver operating characteristic value higher than 80%. The thoracic and the abdominal signals were good parameters for the identification of the occurrence of sleep apnoea, being as quick as the nasal airflow signal. These results suggested that sleep apnoea events could be identified through constant monitoring of the patient's thoracic and abdominal signals. Knowledge of these signals could help nurses to manage sleep apnoea in patients.