Does ECG signal reconstruction and deep transfer learning classification improve automated heart disease diagnosis?
Effective ECG segmentation and deep learning models offer a reliable and accurate solution for automated heart disease diagnosis.
: The results highlight the superiority of deep, feature-rich models in handling reconstructed ECG signals and confirm the value of segmentation as a critical preprocessing step. These findings underscore the importance of effective ECG segmentation in DL applications for automated heart disease diagnosis, offering a more reliable and accurate solution.
Ahmad et al. (Fri,) studied this question.