Comparing Deep Neural Network for Multi-Label ECG Diagnosis From Scanned ECG
This addresses the challenge of integrating automated ECG diagnosis into clinical workflows for healthcare professionals, but it is incremental as it compares existing methods on a new data type.
The study tackled the problem of automated multi-label ECG diagnosis from scanned paper ECGs by comparing deep neural network architectures like AlexNet, VGG, ResNet, and Vision Transformer, finding that these models can extract diagnostic information from scanned images with varying strengths and limitations.
Automated ECG diagnosis has seen significant advancements with deep learning techniques, but real-world applications still face challenges when dealing with scanned paper ECGs. In this study, we explore multi-label classification of ECGs extracted from scanned images, moving beyond traditional binary classification (normal/abnormal). We evaluate the performance of multiple deep neural network architectures, including AlexNet, VGG, ResNet, and Vision Transformer, on scanned ECG datasets. Our comparative analysis examines model accuracy, robustness to image artifacts, and generalizability across different ECG conditions. Additionally, we investigate whether ECG signals extracted from scanned images retain sufficient diagnostic information for reliable automated classification. The findings highlight the strengths and limitations of each architecture, providing insights into the feasibility of image-based ECG diagnosis and its potential integration into clinical workflows.