Work

Artificial Neural Network Comparison on hERG Channel Blockade Detection

Year: 2022

Type: article

Abstract: This work will present a comparison of several Artificial Neural Network methods for a classification problem related to cardiac safety assessment.Given the extracellular field potential recorded by means of micro-electrode arrays, the aim is to determine whether a given chemical drug is altering the electrical activity of cardiomyocytes by disrupting the normal behavior of the hERG channels.To do so, this work has considered four different Neural Network methods and compared them in terms of accuracy and computational costs.The conclusion is that, among the tested architectures, the Multilayer Perceptron (MLP) and multivariate 1-dimensional Convolutional Neural Network (1D-CNN) give the most promising results.

Source: International Journal of Computer Applications

DOI: 10.5120/ijca2022922119

Authors: Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia

Institutions: Institut national de recherche en sciences et technologies du numérique, Inserm, Leiden University Medical Center, Laboratoire de Mathématiques de Versailles

Language: English

Topic: Cardiac electrophysiology and arrhythmias

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: European Commission