Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146826
Title: Deep neural networks and transfer learning on a multivariate physiological signal dataset
Authors: Bizzego, Andrea
Gabrieli, Giulio
Esposito, Gianluca
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Science::Biological sciences::Human anatomy and physiology
Issue Date: 2021
Source: Bizzego, A., Gabrieli, G. & Esposito, G. (2021). Deep neural networks and transfer learning on a multivariate physiological signal dataset. Bioengineering, 8(3), 35-. https://dx.doi.org/10.3390/bioengineering8030035
Project: NAP Start-up Grant M4081597 
Journal: Bioengineering 
Abstract: While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.
URI: https://hdl.handle.net/10356/146826
ISSN: 2306-5354
DOI: 10.3390/bioengineering8030035
DOI (Related Dataset): https://doi.org/10.21979/N9/42BBFA
https://doi.org/10.21979/N9/O9ADTR
https://doi.org/10.21979/N9/YCDXNE
Schools: School of Social Sciences 
Lee Kong Chian School of Medicine (LKCMedicine) 
Departments: Division of Psychology 
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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