Multiple parallel hidden layers autoencoder for denoising ECG signal

Author:

Samann Fars12,Schanze Thomas1

Affiliation:

1. Institute for Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Giesen , Germany

2. Department of Biomedical Engineering, University of Duhok, Duhok , Kurdistan Region, Iraq

Abstract

Abstract Deep learning with multiple hidden layers denoising autoencoders (MHL-DAE) is commonly used to denoise images and signals through dimension reduction. Here, we explore the potential of multiple parallel hidden layers denoising autoencoder (MPHL-DAE) to denoise complex bio-signals, like electrocardiogram (ECG). A merge layer, e.g., average layer is considered as the output of the proposed model by combining the outputs of the parallel hidden layers. The parallel hidden layers in the coding layer with activation function of different scale a, e.g., are considered to capture distinct features of the input. The lower/upper number of the required hidden neurons of the coding layer are estimated using data driven approach via singular values decomposition (SVD). The results show that the proposed MPHL-DAE model achieve better/similar SNR improvement compared to MHL-DAE with suitable scale for various noise levels respectively.

Publisher

Walter de Gruyter GmbH

Subject

Biomedical Engineering

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