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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.02711 |
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| _version_ | 1866910472511422464 |
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| author | Avetisyan, Aram Khachaturov, Nikolas Asatryan, Ariana Tigranyan, Shahane Markin, Yury |
| author_facet | Avetisyan, Aram Khachaturov, Nikolas Asatryan, Ariana Tigranyan, Shahane Markin, Yury |
| contents | Electrocardiogram (ECG) delineation plays a crucial role in assisting cardiologists with accurate diagnoses. Prior research studies have explored various methods, including the application of deep learning techniques, to achieve precise delineation. However, existing approaches face limitations primarily related to dataset size and robustness. In this paper, we introduce a dataset for ECG delineation and propose a novel self-trained method aimed at leveraging a vast amount of unlabeled ECG data. Our approach involves the pseudolabeling of unlabeled data using a neural network trained on our dataset. Subsequently, we train the model on the newly labeled samples to enhance the quality of delineation. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02711 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Trained Model for ECG Complex Delineation Avetisyan, Aram Khachaturov, Nikolas Asatryan, Ariana Tigranyan, Shahane Markin, Yury Machine Learning Signal Processing Electrocardiogram (ECG) delineation plays a crucial role in assisting cardiologists with accurate diagnoses. Prior research studies have explored various methods, including the application of deep learning techniques, to achieve precise delineation. However, existing approaches face limitations primarily related to dataset size and robustness. In this paper, we introduce a dataset for ECG delineation and propose a novel self-trained method aimed at leveraging a vast amount of unlabeled ECG data. Our approach involves the pseudolabeling of unlabeled data using a neural network trained on our dataset. Subsequently, we train the model on the newly labeled samples to enhance the quality of delineation. 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. |
| title | Self-Trained Model for ECG Complex Delineation |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2406.02711 |