Arrhythmia Classification Using the Deep Learning Visual Geometry Group (VGG) Model

Abstract

Cardiovascular disease (CVD) is one of the non-communicable diseases (NCDs) and 32% of the world's people die prematurely due to cardiovascular disease (WHO, 2022). The development of computing technology and artificial intelligence (AI), especially Deep Learning (DL), has contributed significantly to helping medical personnel carry out initial pre-diagnosis and classification of heart disease. In this study, we limit heart rhythm detection research into two categories, namely, Normal (N) and Abnormal (An) which are visualized in a standardized amplitude vs time diagram on the PTBDB dataset. The classification model in this research uses the 1-dimensional Deep Neural Network (1D-DNN) Visual Geometry Group, namely, VGG11, VGG13, VGG16, and VGG19. The denoising technique presented in this study on each ECG data sample thereby improving the quality of training data for the AI detection model. The performance of the VGG16 model shows the best training and validation accuracy with the lowest loss, which is 97.85% accuracy; 97.99% precision; 99.75% recall; and 98.52% f1-score. In this way, medical personnel will be helped more quickly in efforts to prevent and control heart disease that occurs in society, especially in the lower middle class. Further research needs to be done to use VGG with more blocks if the structure of the dataset to be classified is much more complex.