TY - GEN
T1 - Heart Rhythm Classification from Electrocardiogram Signals Using Hybrid PSO-Neural Network Method and Neural ICA
AU - Ariyati, Miftah Rahmalia
AU - Nasution, Aulia
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Studies on the classification of heart rhythms from Electrocardiogram (ECG) signal interpretation have been widely reported. Several techniques for recognizing the abnormalities on left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) using the Taguchi optimization method and the Naïve Bayes classification method have been reported. Unfortunately results from the Naïve Bayes classification method are not as good as those using method such as SVM classification method. In the paper we propose a Hybrid PSO-Neural Network (NN) as a classification method and a Neural Independent Component Analysis (Neural-ICA) as a filter method. Neural ICA aims to separate the original signal and the noise signal on the ECG signal record. In this research the ICA method implements the Neural algorithm for the process of updating the weights after filter process. The Hybrid PSO-Neural Network is a Neural Network method that optimized by PSO to optimize the classification result. Hybrid PSO-NN method can improve the classification accuracy up to 2%, i.e. 99% accuracy, in comparison to NN method 98% accuracy and SVM method 96% accuracy, respectively.
AB - Studies on the classification of heart rhythms from Electrocardiogram (ECG) signal interpretation have been widely reported. Several techniques for recognizing the abnormalities on left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) using the Taguchi optimization method and the Naïve Bayes classification method have been reported. Unfortunately results from the Naïve Bayes classification method are not as good as those using method such as SVM classification method. In the paper we propose a Hybrid PSO-Neural Network (NN) as a classification method and a Neural Independent Component Analysis (Neural-ICA) as a filter method. Neural ICA aims to separate the original signal and the noise signal on the ECG signal record. In this research the ICA method implements the Neural algorithm for the process of updating the weights after filter process. The Hybrid PSO-Neural Network is a Neural Network method that optimized by PSO to optimize the classification result. Hybrid PSO-NN method can improve the classification accuracy up to 2%, i.e. 99% accuracy, in comparison to NN method 98% accuracy and SVM method 96% accuracy, respectively.
KW - ECG signal classification
KW - Independent Component Analysis
KW - Neural Network
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=85066917198&partnerID=8YFLogxK
U2 - 10.1109/ISITIA.2018.8710837
DO - 10.1109/ISITIA.2018.8710837
M3 - Conference contribution
AN - SCOPUS:85066917198
T3 - Proceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
SP - 449
EP - 454
BT - Proceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
Y2 - 30 August 2018 through 31 August 2018
ER -