%0 Conference Paper %1 lassoued2018multiclass %A Lassoued, H. %A Ketata, R. %B 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET) %D 2018 %K classification ecg multi-class network neural %P 473-478 %R 10.1109/ASET.2018.8379901 %T ECG multi-class classification using neural network as machine learning model %U https://ieeexplore.ieee.org/document/8379901 %X The main objective of this paper is to prepare a Clinical Decision Support System (CDSS) for a multi-class classification of ElectroCardioGram (ECG) signals into certain cardiac diseases. This CDSS is based on Artificial Neural Network (ANN) as a machine learning classifier and uses time scale input features. Fourty eight (48) ECG signals were selected from MIT-BIH arrhythmia database, of one minute recording. Unfortunately, among several types of learning algorithms for the ANN classifier, finding the appropriate one demands a comparative study. So, in this study, we have evaluated the impact of two learning algorithms, which are the Levenberg-Marquardt (trainlm) and the Bayesian-Regularization (trainbr) on the proposed ANN performance. Consequently, we have achieved that trainbr reaches the most accurate result (93.8%), while trainlm generates the highest classification speed (0.582s). Subsequently, in order to assess the efficiency of this work, a second comparative study with related works, is done. Therefore, despite not being in the same working conditions, the obtained accuracy (93.8%) is considered acceptable.

@inproceedings{lassoued2018multiclass, abstract = {The main objective of this paper is to prepare a Clinical Decision Support System (CDSS) for a multi-class classification of ElectroCardioGram (ECG) signals into certain cardiac diseases. This CDSS is based on Artificial Neural Network (ANN) as a machine learning classifier and uses time scale input features. Fourty eight (48) ECG signals were selected from MIT-BIH arrhythmia database, of one minute recording. Unfortunately, among several types of learning algorithms for the ANN classifier, finding the appropriate one demands a comparative study. So, in this study, we have evaluated the impact of two learning algorithms, which are the Levenberg-Marquardt (trainlm) and the Bayesian-Regularization (trainbr) on the proposed ANN performance. Consequently, we have achieved that trainbr reaches the most accurate result (93.8%), while trainlm generates the highest classification speed (0.582s). Subsequently, in order to assess the efficiency of this work, a second comparative study with related works, is done. Therefore, despite not being in the same working conditions, the obtained accuracy (93.8%) is considered acceptable.}, added-at = {2020-02-21T17:54:41.000+0100}, author = {{Lassoued}, H. and {Ketata}, R.}, biburl = {https://www.bibsonomy.org/bibtex/247fa9ca4c9ab5c21068083219b34f6cc/nosebrain}, booktitle = {2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET)}, description = {ECG multi-class classification using neural network as machine learning model - IEEE Conference Publication}, doi = {10.1109/ASET.2018.8379901}, interhash = {698827eed2070b3136cb44a3ea449f0d}, intrahash = {47fa9ca4c9ab5c21068083219b34f6cc}, issn = {null}, keywords = {classification ecg multi-class network neural}, month = {March}, pages = {473-478}, timestamp = {2020-02-21T17:54:41.000+0100}, title = {ECG multi-class classification using neural network as machine learning model}, url = {https://ieeexplore.ieee.org/document/8379901}, year = 2018 }