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

Autor:	Hatamian, Faezeh Nejati, Davari, AmirAbbas, Maier, Andreas
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.01192 Zobrazit plný text záznamu View this record from Arxiv