Self-Trained Model for ECG Complex Delineation

no code implementations • 4 Jun 2024 • Aram Avetisyan, Nikolas Khachaturov, Ariana Asatryan, Shahane Tigranyan, Yury Markin

We conduct experiments demonstrating that our dataset is a valuable resource for training robust models and that our proposed self-trained method improves the prediction quality of ECG delineation.

Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification

no code implementations • 19 May 2023 • Aram Avetisyan, Shahane Tigranyan, Ariana Asatryan, Olga Mashkova, Sergey Skorik, Vladislav Ananev, Yury Markin

In this paper, we propose a methodology to improve the quality of heart disease prediction regardless of the dataset by training neural networks on a variety of datasets with further fine-tuning for the specific dataset.

Disease Prediction ECG Classification