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Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer

Research output: Contribution to journalArticlepeer-review

Lilia Sidhom, Ines Chihi, Mahfoudh Barhoumi, Nesrine Ben Afia, Ernest Nlandu Kamavuako, Mohamed Trabelsi

Original languageEnglish
Article number482
Issue number9
PublishedSep 2022

Bibliographical note

Funding Information: This work has been funded in part by KFAS, the Kuwait Foundation for Advancement of Sciences, project no. CN20-13EE-01. Publisher Copyright: © 2022 by the authors.

King's Authors


This paper aims to design a smart biosensor to predict electrocardiogram (ECG) signals in a specific auscultation site from other ECG signals measured from other measurement sites. The proposed design is based on a hybrid architecture using the Artificial Neural Networks (ANNs) model and Taguchi optimizer to avoid the ANN issues related to hyperparameters and to improve its accuracy. The proposed approach aims to optimize the number and type of inputs to be considered for the ANN model. Indeed, different combinations are considered in order to find the optimal input combination for the best prediction quality. By identifying the factors that influence a model’s prediction and their degree of importance via the modified Taguchi optimizer, the developed biosensor improves the prediction accuracy of ECG signals collected from different auscultation sites compared to the ANN-based biosensor. Based on an actual database, the simulation results show that this improvement is significant; it can reach more than 94% accuracy.

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