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- W4281566679 abstract "Cardiovascular diseases represent most common cause of death in both men and women from all around the world. A good recommendation on how to prevent such diseases is to record patient’s ECG and have it examined by a doctor. This paper deals with the classification of ECG recordings focused on binary classification of the given heart rhythm. The proposed classification model is based on the use of deep neural network, specifically 1D convolutional neural network. The parameters of the neural network were discovered using sequential search of hyperspace and free parameters; grid-search. We have achieved 92 % in classification accuracy." @default.
- W4281566679 created "2022-05-27" @default.
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- W4281566679 date "2022-03-02" @default.
- W4281566679 modified "2023-09-28" @default.
- W4281566679 title "Using Neural Networks for ECG Classification" @default.
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- W4281566679 doi "https://doi.org/10.1109/sami54271.2022.9780670" @default.
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