ATRIAL FIBRILLATION DETECTION ON ELECTROCARDIOGRAMS WITH CONVOLUTIONAL NEURAL NETWORKS

Viktor Kifer


Ternopil Ivan Puluj National Technical University (Ukraine)
http://orcid.org/0000-0002-0621-9121

Natalia Zagorodna

zagorodna.n@gmail.com
Ternopil Ivan Puluj National Technical University (Ukraine)
http://orcid.org/0000-0002-1808-835X

Olena Hevko


Ternopil Ivan Puluj National Technical University (Ukraine)
http://orcid.org/0000-0003-1427-1699

Abstract

In this paper, we present our research which confirms the suitability of the convolutional neural network usage for the classification of single-lead ECG recordings. The proposed method was designed for classifying normal sinus rhythm, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy signals. The method combines manually selected features with the features learned by the deep neural network. The Physionet Challenge 2017 dataset of over 8500 ECG recordings was used for the model training and validation. The trained model reaches an average F1-score 0.71 in classifying normal sinus rhythm, AF and other rhythms respectively.


Keywords:

electrocardiography, machine learning, neural networks

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Published
2019-12-15

Cited by

Kifer, V., Zagorodna, N., & Hevko, O. (2019). ATRIAL FIBRILLATION DETECTION ON ELECTROCARDIOGRAMS WITH CONVOLUTIONAL NEURAL NETWORKS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(4), 69–73. https://doi.org/10.35784/iapgos.116

Authors

Viktor Kifer 

Ternopil Ivan Puluj National Technical University Ukraine
http://orcid.org/0000-0002-0621-9121

Authors

Natalia Zagorodna 
zagorodna.n@gmail.com
Ternopil Ivan Puluj National Technical University Ukraine
http://orcid.org/0000-0002-1808-835X

Authors

Olena Hevko 

Ternopil Ivan Puluj National Technical University Ukraine
http://orcid.org/0000-0003-1427-1699

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