This document summarizes a study that used principal component analysis (PCA) and kernel principal component analysis (KPCA) to extract features from electrocardiogram (ECG) signals, which were then classified using a binary support vector machine (SVM) model. The study tested PCA, KPCA, and no feature extraction on ECG data from the MIT-BIH Arrhythmia Database to classify normal signals and three types of abnormalities. Results showed that combining SVM with KPCA feature extraction achieved the best classification performance compared to using SVM alone or with PCA. Automatic ECG classification is important for diagnosing cardiac irregularities.

1. PCA AND KPCA OF ECG
SIGNALS WITH BINARY SVM
CLASSIFICATION
Author:Maya Kallas, Clovis Francis, Lara Kanaan, Dalia Merheb,
Paul Honeine, Hassan Amoud
Source:Signal Processing Systems (SiPS), 2011 IEEE Workshop
Advisor:Yin-Fu Huang
Student:YU-HSIEN CHO

2. OUTLINE
I. Abstract
II. Introduction
III. Methods
IV. Experiment and Result
V. Conclusion

3. I. Abstract
 Cardiovasculardiseases remain the primary cause of death
around the world. In Lebanon, 63.13% of men and 56.57%of
women aged 50 years and above die from heart diseases[1].
 For effective diagnostics, the study of the ECG signal must be
carried out for several hours.
 Automatic detection and classification of electrocardiogram
(ECG) signals are important for diagnosis of cardiac
irregularities.

4. II. Introduction
 In this paper, we propose to combine the Support Vector
Machines used in classification on one hand, with the
Principal Component Analysis used in order to reduce the
size of the data by choosing some axes that capture the most
variance between data.
 On the other hand, with the kernel principal component
analysis where a mapping to a high dimensional space is
needed to capture the most relevant axes but for nonlinear
separable data.
 The efficiency of the proposed SVM classification is
illustrated on real electrocardiogram dataset taken from
MIT-BIH Arrhythmia Database.

5.  Training data sets:MIT-BIH arrhythmia database
 inputs:10 normal signals,
10 PVC signals,
14 LBBB signals
 4Classes:
 TP:abnormalabnormal , FP:normalabnormal
FN:abnomalnormal , TN:normalnormal

6. III. Methods
PCA feature extraction:
The analyzed signal is a single lead ECG which has been
converted into a data matrix X.
where M is the number of beats
Step1. Getting the data matrix X
Step2. Subtract the mean
Step3. Calculate the covariance matrix
Step4. Calculate the Eigenvectors/Eigenvalues of the
covariance matrix

7. Step5. Choosing principal components and forming
feature vector
Step6. Deriving the new data set :
Final Data = (row feature vector) ⋅ (row data adjust)
Step7. Calculate the reconstruction

8.  KPCA feature extraction :
The basic idea of KPCA is to map the original data into a
high dimensional space via a specific function and then to
apply the standard PCA algorithm on it.
 Since the ECG signal has a lot of nonlinear structures and
since the PCA extracts only the linear ones, we adopted
KPCA to extract the nonlinear components.

9. IV. Experiment and Result
 The kernel functions used are the linear, polynomial and the
Gaussian, where x and y refer to vectors of x i and y i
components respectively.
 Table 1 shows the parameters considered as best models.
kernel parameters σ=0.8,
regularization constant C =100

10. Experiment and Result
 Resultsof applying the Gaussian classifier on the data is
presented in Figure 1.

11. Experiment and Result
 As
we can see in Table 2, the results achieved by the
Gaussian classifier were better than those achieved by the
SVM linear and the SVM-poly.

12. Experiment and Result
 PCA uses linear transformation to convert a large amount of
correlated variables to a smaller number of uncorrelated
principal components that preserve most of the useful
information. See the Figure 2

13. Experiment and Result
 Figure3 illustrates the results of applying the binary SVM
combined with KPCA. So we can conclude that for binary
ECG classification the most convenient method is combining
SVM with KPCA.

14. Experiment and Result
 Thebest results obtained in SVM by using the original input
without feature extraction, and by using
PCA (σ=10, C =40) and KPCA (σ=0.5, C =10) feature
extraction are given in table 3.

15. V. Conclusion
 Electrocardiogram (ECG) supervising is the most important
and efficient way for preventing heart attacks. Our work
presents an integrated classification method, which combines
the Support Vector Machine (SVM) with either the Principal
Component Analysis (PCA) or the Kernel PCA (KPCA) for
classification of different types of cardiac abnormalities.
 Our experiments show that binary SVM combined with
feature extraction using PCA or KPCA greatly improves the
quality of classification than that without feature extraction.

16. Fig. 4 Electrocardiogram