Isa, Sani Muhamad

Binus University Repository

Sleep Apnea Detection from ECG SignalAnalysis on Optimal Features, Principal Components, and Nonlinearity Sani M. Isa, Mohamad Ivan Fanany, Wisnu Jatmiko, Aniati Murni Arymurthy Faculty of Computer Science University of Indonesia Depok, Indonesia sani.muhamad@ui.ac.id, ivan, wisnu, aniati{@cs.ui.ac.id}    Abstract—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.  Keywords: feature selection, sleep apnea, PCA, nonlinearity I.  INTRODUCTION To date 84 kind of sleep disorders have been discovered, where insomnia, sleep apnea, narcolepsy, and restless leg syndrome are the most common sleep disorders [1].  Sleep apnea characterized by abnormal pauses in breathing or instances of abnormally low breathing during sleep. Each pause in breathing, called an apnea, can last from a few seconds to minutes, and may occur 5 to 30 times or more an hour. Similarly, each abnormally low breathing event is called a hypopnea. Sleep apnea is typically divided into two classes; central sleep apnea (CSA) in which respiratory drive is absent or inhibited, and obstructive sleep apnea (OSA) in which upper airway collapse is responsible for disrupted respiration. While central events are often seen in subjects with OSA, pure CSA is relatively rare. OSA, however, is not a rare condition. It occurs in 2% to 4% of middle-aged adults and in 1% to 3% of preschool children. Overall it is estimated that there are 10 to 20 million sufferers in the U.S. However, despite the fact that apnea has such health and quality of life implications, there is a surprisingly low public and medical awareness of the illness. Of the 10 to 20 million sufferers in the U.S., it is estimated that only 10 to 15% have been diagnosed [2] Polysomnography (PSG) has become standard in diagnosing sleep disorders, including sleep apnea. PSG include recording of breath airflow, respiratory movement, oxygen saturation, electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG), as well as body position. PSG performed in the laboratory for a full night sleep under doctors and nurses supervision [3]. Although PSG has been recognized as the golden standard for diagnosing sleep apnea, PSG got many criticisms from some researchers. This is due to several reasons, including uncomfortable, expensive, and limited avalailability [4].  Therefore a need for simpler technology that has the same reliability with PSG that doesn’t require special laboratory. ECG recording is one of the simpler and efficient technology in sleep disorders detection. Cyclic variations in RR intervals (beat to beat heart rate) of ECG signals have been reported to be associated with sleep apnea events; this consists of bradycardia during apnea followed by tachycardia upon its cessation. This pattern had been successfully used to detect patients with clinical symptoms of sleep apnea [4]. Various studies have confirmed that several new methods could possibly recognize sleep apnea from heart rate variability (HRV) changes [5]. In 2004, Chazal, suggested an obstructive sleep apnea detection using statistical measurement of variables derived from RR-intervals and ECG-derived respiratory signal (EDR) [4]. In 2010, Yilmaz conducting research for the sleep stages classification and apnea using a single lead ECG. They used three features derived from the RR-interval, i.e. median, inter-quartile range (IQR), and the mean absolute deviation (MAD). Classification is performed using kNN, Quadratic Discriminant Analysis (QDA), and Support Vector Machines (SVM) [6]. In previous study we used the Principal Component Regression to predict AHI (Apnea-Hipoapnea Index) value based on features proposed by Chazal and Yilmaz. The study shows there are multicollinearity between the features [7]. Based on those findings, we try to apply PCA transform on Chazal and Yilmaz features for sleep apnea classification. PCA tranformation would eliminate the problem of multicollinearity in order to obtain a more compact features and better classification accuracy. Moreover, in this study we are trying to determine the best model for sleep apnea classification by focusing on following issues: - What are the optimal features among Chazal, Yilmaz, and PCA features? - How many features are sufficient? - How significant is the non-linear kernel than the linear kernel in SVM classification? 978-1-4244-5089-3/11/$26.00 ©2011 IEEE- What is the optimal classification method among the kNN, Naïve-Bayes, and SVM? - How applicable is the proposed system to classify sleep apnea? II. METHODOLOGY The schematic diagram of the system which is used in this study is shown in Fig. 1. To obtain the RR-interval, QRS complex in ECG signal was detected using PhysioToolkit Library [8]. RR-interval can be calculated based on the beat to beat annotation, but only RR-interval which has value between 0.5 and 1.5 will be processed [6]. In this study, we are examining two feature extraction approaches in selecting the optimal features, the approaches using the features suggested by Chazal [4], and the features suggested by Yilmaz [6]. We are doing feature extraction for each epoch which has 1 minute duration. To overcome multicollinearity problem, the results of feature extraction will be  transformed into the orthogonal features using the PCA transform. To examine training data, we use k-fold cross validation with the number of folds = 10, and repeated random subsampling with repeat train/test = 10 where the proportion of validation test and training data is 25:75. Next, we apply self organizing map (SOM) or Kohonen network [9] clustering to select the most representative data from training set. We choose five cluster with the largest number of member.  The next step we classify each epoch using kNN, Naive-Bayes, and SVM with linear and RBF kernel to determine whether the epoch is apnea (A) or no-apnea (N) category. Then, we evaluate the result of classification by calculating classification accuracy. A. Subjects The database of ECG signals in the “2000 Computers in Cardiology Conference Challenge” was used in this study. It consists of 70 recordings, containing a single ECG signal digitized at 100 Hz with 12-bit resolution, continuously for approximately 8 hours (individual recordings vary in length from less than 7 hours to nearly 10 hours) [10]. Each recording includes a set of reference annotations, one for each minute of the recording (epoch), indicate the presence or absence of apnea during that minute. These reference annotations were made by human experts on the basis of simultaneously recorded respiration signals. In this study, we use only 35 record of the database which is consists of 17,013 epoch. B. Feature Extraction For each epoch following features were calculated:  - Mean and standard deviation RR-interval. - NN50 (variant 1): number of pairs of adjacent RR-intervals where the first RR-interval exceeds the second RR-interval by more than 50 ms. - NN50 (variant 2): 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: each NN50 measure divided by the total number of RR-intervals. - SDSD: standard deviation of the differences between adjacent RR-intervals. - RMSSD: square root of the mean of the sum of the squares of differences between adjacent RR-intervals - Median of RR-interval. - Inter-quartile range (IQR): difference between 75th and 25th percentiles of the RR-interval value distribution. - Mean absolute deviation values (MAD): subtraction of the mean RR-interval values from all the RR-interval values in an epoch. The first eight features are proposed by Chazal[4], while the three latter features are proposed by Yilmaz who claimed that RR-interval mean, standard deviation, and range are  sensitive to outliers, and thus classification performance deteriorates when theses features are included[6]. C. PCA PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate, the second greatest variance on the second coordinate, and so on. Define a data matrix, XT, with zero empirical mean, where each of the n rows represents a different repetition of the experiment, and each of the m columns gives a particular kind of datum. The PCA transformation is then given by: TTTVWXYΣ==  (1)  Figure 1.  Schematic diagram of the system where the matrices W, Σ, and V are given by a singular value decomposition (SVD) of X as WΣVT. (V is not uniquely defined in the usual case when m<n−1, but Y will usually still be uniquely defined) Σ is an m-by-n diagonal matrix with nonnegative real numbers on the diagonal. Since W (by definition of the SVD of a real matrix) is an orthogonal matrix, each row of YT is simply a rotation of the corresponding row of XT. The first column of YT is made up of the "scores" of the cases with respect to the "principal" component, the next column has the scores with respect to the "second principal" component, and so on. If we want a reduced-dimensionality representation, we can project X down into the reduced space defined by only the first L singular vectors, WL: TLLTL VXWY Σ==  (2) The matrix W of singular vectors of X is equivalently the matrix W of eigenvectors of the matrix of observed covariances C = XXT, TTT WWXX ΣΣ=  (3) Given a set of points in Euclidean space, the first principal component corresponds to a line that passes through the multidimensional mean and minimizes the sum of squares of the distances of the points from the line. The second principal component corresponds to the same concept after all correlation with the first principal component has been subtracted out from the points. The singular values (in Σ) are the square roots of the eigenvalues of the matrix XXT. Each eigenvalue is proportional to the portion of the "variance" that is correlated with each eigenvector. The sum of all the eigenvalues is equal to the sum of the squared distances of the points from their multidimensional mean. PCA essentially rotates the set of points around their mean in order to align with the principal components. This moves as much of the variance as possible into the first few dimensions. The values in the remaining dimensions, therefore, tend to be small and may be dropped with minimal loss of information. PCA is often used in this manner for dimensionality reduction. PCA has the distinction of being the optimal orthogonal transformation for keeping the subspace that has largest "variance" [11]. III. RESULTS AND DISCUSSION The QRS detection process gives an average accuracy of RR-interval 99.54% and 0.46% of the RR-interval should be excluded because its value is less than 0.5 and greater than 1.5. SOM clustering produces sample 54.9% of original data size. The percentage of epochs with apnea (A) annotations on the sample training data is 33.1%, and the remaining  with no-apnea annotations (N) is 66.9%. Because the distribution of A and N are not balance then we apply random undersampling technique to balance the data by select 50% of the no-apnea data randomly. Table I shows the classification accuracy of the model selection using kNN, Naïve Bayes, and SVM based on Chazal and Yilmaz et al features. Table II shows the classification accuracy based on Chazal and Yilmaz features. From Table I, SVM with linear kernel shows the best classification accuracy on Cross Validation and Random Sampling model using Chazal features, while for Yilmaz features the best accuracy is achieved by the Naïve-Bayes method. The method of kNN shows the best classification accuracy on Test on Train Data model using Chazal and Yilmaz features. From Table II, SVM with linear kernel shows the best classification accuracy using Chazal features, while for Yilmaz features the best classification accuracy is achieved by the SVM with RBF kernel. Cross Validation and Random Sampling model have better classification accuracy prediction than Test on Train Data. The experimental results in tables I and II show that the SVM with linear kernel give better classification accuracy than SVM with RBF kernel in classification based on Chazal features. Otherwise, SVM with RBF kernel gives better classification accuracy than SVM with linear kernel in classification based on Yilmaz features. This fact indicates that the relation between Chazal features with the target class (i.e. apnea or no-apnea) tends to be linear, whereas the relation between Yilmaz features with the target class are non-linear. To find out how PCA transformation could improve the classification accuracy, we perform apnea classification using PC as a features. In this experiments we examine the using of 2 to 11 PC. Table III shows the classification accuracy of the model selection using kNN, Naïve Bayes, and SVM. Table IV shows the classification accuracy based on this features. From Table III, we see that classification accuracy is better if using more PC as a feature. SVM with RBF kernel shows the best classification accuracy on the Cross Validation and Random Sampling model, while kNN shows the best performance on test on Train Data. Using 5 PC as a feature on Cross Validation and Random Sampling model give better classification accuracy than Chazal features. From Table IV, we see that SVM with RBF kernel shows the best classification accuracy in all classification methods. From the experimental results, we obtain the fact that TABLE I.  ACCURACY OF MODEL SELECTION BASED ON CHAZAL AND YILMAZ FEATURES Features Model Method kNN N-Bayes Linear SVM RBF SVM Chazal CV 0.6777 0.6197 0.7093 0.5969 RS 0.6668 0.6213 0.7074 0.5920 TT 0.9399 0.6333 0.7155 0.6243 Yilmaz CV 0.6469 0.6595 0.5530 0.6381 RS 0.6422 0.6566 0.5268 0.6319 TT 0.8347 0.6644 0.5511 0.6423 CV: cross validation, RS: random sampling, TT: test on train data TABLE II.  CLASSIFICATION ACCURACY BASED ON CHAZAL AND YILMAZ FEATURES Features Method kNN N-Bayes Linear SVM RBF SVM Chazal 0.6584 0.6327 0.7078 0.5897 Yilmaz 0.5 0.5 0.5693 0.6429  classification accuracy of linear SVM kernel is the best applied to Chazal features, on the contrary RBF kernel is more suitable for Yilmaz and PCA features. Classification based on kNN, Naïve-Bayes, and SVM with linear kernel shows the best accuracy using 9 PC, while SVM with RBF kernel gives the best classification accuracy using 7 PC as a features. The experimental results show that the relation between PCA features with target class (i.e. apnea or no-apnea) tend to be non-linear. IV. CONCLUSION AND FUTURE WORKS In this paper we have applied PCA transform to Chazal and Yilmaz features on sleep apnea detection using ECG signal. PCA has shown the powerful tool for feature extraction and dimension reduction. PCA eliminates redundant features and improves the accuracy of the classification method. The implementation of PCA transform has shown more efficient than Chazal and Yilmaz features in term of number of feature and classification accuracy. Furthermore, SVM with RBF kernel shows the best classification accuracy on all features i.e. Chazal, Yilmaz, and PCA. In  the future, we are planning to use PCA transformation on sleep stage detection using ECG signal. REFERENCES [1] “Sleep Disorder Overview”. HealthCommunities. Available: http://www.neurologychannel.com/sleepdisorders/index.shtml [2] "Sleep Apnea: What Is Sleep Apnea?". NHLBI: Health Information for the Public. U.S. Department of Health and Human Services. Available: http://www.nhlbi.nih.gov/health/dci/Diseases/SleepApnea/SleepApnea_WhatIs.html. [3] Armon C, Johnson G.K, Roy A, Nowack W.J, “Polysomnograph”. Available: http://emedicine.medscape.com/article/1188764-overview [4] Chazal P, Penzel T, and Heneghan C,  “Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram”. Physiological Measurement. July 2004; 25: 967-983. [5] T. Penzel, J. McNames, P. de Chazal, B. Raymond, A. Murray, G. Moody, “Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings”, Medical and Biological Engineering and Computing. 2002; 40:402–407. [6] Yilmaz B, Asyali MH, Arikan E, Yetkin S, Ozgen F. “Sleep stage and obstructive apneaic epoch classification using single-lead ECG“. Biomed Eng Online. 2010 Aug 19;9(1):39. [7] Sidik M., Sani M. Isa, M. Ivan Fanany, Wisnu J., and T. Basaruddin. “Principal Component Regression Analysis on Automatic Sleep Apnea Detection from ECG Data”. Proceeding of International Conference on Advance Computer Science and Information Sistem, Nov. 2010 [8] “WFDB Software Package”. PhysioNet. Available: “http://physionet.org/physiotools/wfdb.shtml#library [9] Kohonen, T. and Honkela, T. "Kohonen Network". Scholarpedia. 2007 [10] Y Ichimaru, GB Moody. Development of the polysomnographic database on CD-ROM. Psychiatry and Clinical Neurosciences 53:175-177. 1999 April [11] Jolliffe, I. T. “Principal Component Analysis”. Springer-Verlag. 1986; pp. 487  TABLE III.  ACCURACY OF MODEL SELECTION  BASED ON PCA FEATURES Number of PC Model Method kNN N-Bayes Linear SVM RBF SVM 2 CV 0.5882 0.6139 0.5355 0.6252  RS 0.5901 0.6195 0.5429 0.6284  TT 0.9857 0.6179 0.5398 0.6308 3 CV 0.6611 0.6123 0.5403 0.7086  RS 0.6583 0.6227 0.5486 0.7089  TT 0.9853 0.6218 0.5405 0.7198 4 CV 0.6593 0.6420 0.5502 0.7189  RS 0.6611 0.6492 0.5552 0.7129  TT 0.9871 0.6533 0.5488 0.7293 5 CV 0.6837 0.6708 0.6614 0.7599  RS 0.6762 0.6754 0.6589 0.7425  TT 0.9878 0.6789 0.6694 0.7594 6 CV 0.6901 0.6727 0.6469 0.7502  RS 0.6867 0.6768 0.6471 0.7420  TT 0.9862 0.6812 0.6496 0.7601 7 CV 0.7058 0.7056 0.7099 0.7610  RS 0.7064 0.7076 0.7098 0.7546  TT 0.9869 0.7136 0.7116 0.7707 8 CV 0.7044 0.7127 0.7067 0.7606  RS 0.7043 0.7110 0.7087 0.7567  TT 0.9871 0.7194 0.7086 0.7716 9 CV 0.7097 0.7079 0.7058 0.7608  RS 0.7158 0.7123 0.7074 0.7556  TT 0.9862 0.7192 0.7060 0.7714 10 CV 0.7164 0.7111 0.7060 0.7777  RS 0.7076 0.7145 0.7073 0.7564  TT 0.9869 0.7226 0.7088 0.7716 11 CV 0.7186 0.7100 0.7072 0.7798  RS 0.7004 0.7155 0.7074 0.7565  TT 0.9871 0.7235 0.7086 0.7723 CV: cross validation, RS: random sampling, TT: test on train data TABLE IV.  CLASSIFICATION ACCURACY BASED ON PCA FEATURES Number of PC Method kNN N-Bayes Linear SVM RBF SVM 2 0.6144 0.6042 0.5542 0.6300 3 0.6681 0.6026 0.5585 0.7320 4 0.6649 0.6423 0.5575 0.7266 5 0.6837 0.6708 0.6826 0.7599 6 0.7025 0.6740 0.6622 0.7642 7 0.7164 0.7014 0.7154 0.7814 8 0.7266 0.7137 0.7175 0.7814 9 0.7288 0.7143 0.7180 0.7793 10 0.7164 0.7111 0.7159 0.7782 11 0.7186 0.7100 0.7154 0.7803 

Sleep Apnea Detection from ECG Signal: Analysis on Optimal Features, Principal Components, and Nonlinearity

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Sleep Apnea Detection from ECG Signal: Analysis on Optimal Features, Principal Components, and Nonlinearity

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