ArticleBioengineering (Basel, Switzerland)2021
Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset.
Article in Bioengineering (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Clinical use of electrodermal activity and current developments.European biophysics journal : EBJ · 2026Review
- Biotechnological Interventions for the Production of Subunit Vaccines Against Group A Rotavirus.Cell biochemistry and function · 2024Review
- Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions.Bioengineering (Basel, Switzerland) · 2023Article
- Advances in Multivariate and Multiscale Physiological Signal Analysis.Bioengineering (Basel, Switzerland) · 2022Article
- A Survey on Physiological Signal-Based Emotion Recognition.Bioengineering (Basel, Switzerland) · 2022Article
- Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets.Bioengineering (Basel, Switzerland) · 2021Article
- Acquisition and Processing of Brain Signals.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
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.
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