c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 4... more c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 40-49 Heart rate variability Non-linear analysis Paroxysmal atrial fibrillation Prediction Spectrum Support vector machines a b s t r a c t
This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is b... more This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. This method consists of four steps: preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 4... more c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 40-49 Heart rate variability Non-linear analysis Paroxysmal atrial fibrillation Prediction Spectrum Support vector machines a b s t r a c t
This paper explains an atrial fibrillation (AF) detection algorithm, which consists of a linear d... more This paper explains an atrial fibrillation (AF) detection algorithm, which consists of a linear discriminant analysis (LDA) based feature reduction scheme and a support vector machine (SVM) based classifier. Initially nine features were extracted from the input episodes each containing 32 RR intervals by linear and nonlinear methods. Next, to improve the learning efficiency of the classifier and to reduce the learning time, these features are reduced to 4 features by LDA. The performance of the proposed method in discriminating AF episodes was evaluated using MIT-BIH arrhythmia database. The obtained sensitivity, specificity and positive predictivity were 99.07%, 100% and 100%, respectively.
In this paper, we proposed an algorithm for arrhythmia classification, which is associated with t... more In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were 99.307%, 99.274%, 99.854%, 98.344%, 99.441% and 99.883%, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 4... more c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 40-49 Heart rate variability Non-linear analysis Paroxysmal atrial fibrillation Prediction Spectrum Support vector machines a b s t r a c t
This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is b... more This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. This method consists of four steps: preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 4... more c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 5 ( 2 0 1 2 ) 40-49 Heart rate variability Non-linear analysis Paroxysmal atrial fibrillation Prediction Spectrum Support vector machines a b s t r a c t
This paper explains an atrial fibrillation (AF) detection algorithm, which consists of a linear d... more This paper explains an atrial fibrillation (AF) detection algorithm, which consists of a linear discriminant analysis (LDA) based feature reduction scheme and a support vector machine (SVM) based classifier. Initially nine features were extracted from the input episodes each containing 32 RR intervals by linear and nonlinear methods. Next, to improve the learning efficiency of the classifier and to reduce the learning time, these features are reduced to 4 features by LDA. The performance of the proposed method in discriminating AF episodes was evaluated using MIT-BIH arrhythmia database. The obtained sensitivity, specificity and positive predictivity were 99.07%, 100% and 100%, respectively.
In this paper, we proposed an algorithm for arrhythmia classification, which is associated with t... more In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were 99.307%, 99.274%, 99.854%, 98.344%, 99.441% and 99.883%, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.
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Papers by Maryam Mohebbi