TY - GEN
T1 - A Deep Learning-Based Algorithm for ECG Arrhythmia Classification
AU - Espin-Ramos, Daniela
AU - Alvarado, Vicente
AU - Anazco, Edwin Valarezo
AU - Flores, Erick
AU - Nunez, Bolivar
AU - Santos, Jose
AU - Guerrero, Sara
AU - Aviles-Cedeno, Jonathan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper aims to automatically classify five classes of arrhythmia present in Electrocardiograms (ECG) by using two Deep Learning (DL)-based models. One based on Convolutional Neural Network (CNN) and the other based on Residual Networks (ResNet). The main motivation of this research is to enhance the field of medicine and assist doctors in the diagnosis of arrhythmia. The DL-based models were trained using the MIT Arrhythmia database. The evaluation of the DL-based models was performed by separating the data into 70% for training, 20% for testing and 10% for validation. Results with the testing dataset show an accuracy of 96.33 % and 95.40%; a F1-Score value of 96.34% and 95.34% for the CNN-And ResNet-based models, respectively. With the validation dataset, CNN-based model achieved an accuracy of 99.32% and a F1-Score of 99.32%; ResNet-based model achieved 98.55% and 98.55% for accuracy and F1-Score, respectively.
AB - This paper aims to automatically classify five classes of arrhythmia present in Electrocardiograms (ECG) by using two Deep Learning (DL)-based models. One based on Convolutional Neural Network (CNN) and the other based on Residual Networks (ResNet). The main motivation of this research is to enhance the field of medicine and assist doctors in the diagnosis of arrhythmia. The DL-based models were trained using the MIT Arrhythmia database. The evaluation of the DL-based models was performed by separating the data into 70% for training, 20% for testing and 10% for validation. Results with the testing dataset show an accuracy of 96.33 % and 95.40%; a F1-Score value of 96.34% and 95.34% for the CNN-And ResNet-based models, respectively. With the validation dataset, CNN-based model achieved an accuracy of 99.32% and a F1-Score of 99.32%; ResNet-based model achieved 98.55% and 98.55% for accuracy and F1-Score, respectively.
KW - Arrhythmia Classification
KW - CNN
KW - Deep Learning
KW - ECG
KW - Electrocardiogram
KW - ResNet
UR - https://www.scopus.com/pages/publications/85166671107
U2 - 10.1109/ICPRS58416.2023.10179058
DO - 10.1109/ICPRS58416.2023.10179058
M3 - Contribución a la conferencia
AN - SCOPUS:85166671107
T3 - 2023 IEEE 13th International Conference on Pattern Recognition Systems, ICPRS 2023
BT - 2023 IEEE 13th International Conference on Pattern Recognition Systems, ICPRS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Pattern Recognition Systems, ICPRS 2023
Y2 - 4 July 2023 through 7 July 2023
ER -