12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes
| Autor: | Judyta Salamon, Przemyslaw Wiszniewski, Michal Lepek, Katarzyna Muter, Dorota Kokosinska, Antonina Pater, Mateusz Soliński, Zuzanna Puzio |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Training set Computer science business.industry 12 lead ecg Pattern recognition 030204 cardiovascular system & hematology Fast algorithm Convolutional neural network 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Ranking Test score Artificial intelligence Noise (video) Ecg signal business |
| Zdroj: | CinC |
| ISSN: | 2325-887X |
| DOI: | 10.22489/cinc.2020.124 |
| Popis: | The growing demand for diagnosing of cardiovascular diseases leads to the development of new solutions for automatic classification of recorded ECG signals. Creating a robust and fast algorithm for automatic classification of ECG signal is crucial to improve the quality of healthcare, especially in countries where a lack of experienced specialists is an issue or the healthcare system is overloaded. The aim of the PhysioNet/Computing in Cardiology Challenge 2020 is to create an algorithm for classification of 12-lead ECGs based on ECG signals from multiple databases across the world. The shared training set consisted of 43,101 ECG recordings lasting from 5 to 1800 seconds. We (BioS Team) proposed the machine learning algorithm based on convolutional neural networks. The ECG signals were pre-processed using moving median filters to remove high-frequency noise and baseline wandering. We developed simply convolutional neural network consisting of four main convolutional blocks and one fully connected layer. We achieved a challenge validation score of 0.349, and full test score of 0.279, placing us 14 out of 41 in the official ranking. |
| Databáze: | OpenAIRE |
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