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arXiv · 2009.01192

The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks

Faezeh Nejati Hatamian·AmirAbbas Davari·Andreas Maier
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Abstract

Due to the multiple imperfections during the signal acquisition, Electrocardiogram (ECG) datasets are typically contaminated with numerous types of noise, like salt and pepper and baseline drift. These datasets may contain different recordings with various types of noise [1] and thus, denoising may not be the easiest task. Furthermore, usually, the number of labeled bio-signals is very limited for a proper classification task.

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Faezeh Nejati Hatamian, AmirAbbas Davari, Andreas Maier. 2020-08-14. The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks. https://arxiv.org/abs/2009.01192

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