Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms.

ABSTRACT: This study aimed to develop a bimodal convolutional neural network (CNN) by co-training grayscale images and scalograms of ECG for cardiovascular disease classification. The bimodal CNN model was developed using a 12-lead ECG database collected from Chapman University and Shaoxing People's Hospital. The preprocessed database contains 10,588 ECG data and 11 heart rhythms labeled by a specialist physician. The preprocessed one-dimensional ECG signals were converted into two-dimensional grayscale images and scalograms, which are fed simultaneously to the bimodal CNN model as dual input images. The proposed model aims to improve the performance of CVDs classification by making use of ECG grayscale images and scalograms. The bimodal CNN model consists of two identical Inception-v3 backbone mode

SUBMITTER: Yoon T 

PROVIDER: S-EPMC9941114 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature