Nurul Ashikin Abdul-Kadir1, Norlaili Mat Safri2, Mohd Afzan Othman1. 1. Department of Electronic and Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Johor, Malaysia. 2. Department of Electronic and Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Johor, Malaysia. Electronic address: norlaili@utm.my.
Abstract
BACKGROUND: Atrial fibrillation (AF) can cause the formation of blood clots in the heart. The clots may move to the brain and cause a stroke. Therefore, this study analyzed the ECG features of AF and normal sinus rhythm signals for AF recognition which were extracted by using a second-order dynamic system (SODS) concept. OBJECTIVE: To find the appropriate windowing length for feature extraction based on SODS and to determine a machine learning method that could provide higher accuracy in recognizing AF. METHOD: ECG features were extracted based on a dynamic system (DS) that uses a second-order differential equation to describe the short-term behavior of ECG signals according to the natural frequency (ω), damping coefficient, (ξ), and forcing input (u). The extracted features were windowed into 2, 3, 4, 6, 8, and 10 second episodes to find the appropriate windowing size for AF signal processing. ANOVA and t-tests were used to determine the significant features. In addition, pattern recognition machine learning methods (an artificial neural network (ANN) and a support vector machine (SVM)) with k-fold cross validation (k-CV) were used to develop the ECG recognition system. RESULTS: Significant differences (p < 0.0001) were observed among all ECG groups (NSR, N, AF) using 2, 3, 4 and 6 second episodes for the features ω and u/ω; 4, 6 and 8 second episodes for features ω and u; 4 and 6 second episodes for features ω, u and u/ω, and; 10 second episodes for the feature ξ. The highest accuracy for AF recognition (AF, NSR) using ANN with k-CV was 95.3% using combination of features (ω and u; ω, u and u/ω) and SVM with k-CV was 95.0% using a combination of features ω, u and u/ω. CONCLUSION: This study found that 4 s is the most appropriate windowing length, using two features (ω and u) for AF detection with an accuracy of 95.3%. Moreover, the pattern recognition learning machine uses an ANN with 10-fold cross validation based on DS.
BACKGROUND:Atrial fibrillation (AF) can cause the formation of blood clots in the heart. The clots may move to the brain and cause a stroke. Therefore, this study analyzed the ECG features of AF and normal sinus rhythm signals for AF recognition which were extracted by using a second-order dynamic system (SODS) concept. OBJECTIVE: To find the appropriate windowing length for feature extraction based on SODS and to determine a machine learning method that could provide higher accuracy in recognizing AF. METHOD: ECG features were extracted based on a dynamic system (DS) that uses a second-order differential equation to describe the short-term behavior of ECG signals according to the natural frequency (ω), damping coefficient, (ξ), and forcing input (u). The extracted features were windowed into 2, 3, 4, 6, 8, and 10 second episodes to find the appropriate windowing size for AF signal processing. ANOVA and t-tests were used to determine the significant features. In addition, pattern recognition machine learning methods (an artificial neural network (ANN) and a support vector machine (SVM)) with k-fold cross validation (k-CV) were used to develop the ECG recognition system. RESULTS: Significant differences (p < 0.0001) were observed among all ECG groups (NSR, N, AF) using 2, 3, 4 and 6 second episodes for the features ω and u/ω; 4, 6 and 8 second episodes for features ω and u; 4 and 6 second episodes for features ω, u and u/ω, and; 10 second episodes for the feature ξ. The highest accuracy for AF recognition (AF, NSR) using ANN with k-CV was 95.3% using combination of features (ω and u; ω, u and u/ω) and SVM with k-CV was 95.0% using a combination of features ω, u and u/ω. CONCLUSION: This study found that 4 s is the most appropriate windowing length, using two features (ω and u) for AF detection with an accuracy of 95.3%. Moreover, the pattern recognition learning machine uses an ANN with 10-fold cross validation based on DS.