Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

Bioengineering

Andrea Bizzego¹  Gianluca Esposito¹  Giulio Gabrieli² 

[1] Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto (Trento), Italy;Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;

关键词: multivariate data;    physiological signals;    signal processing;    artificial intelligence;    deep neural networks;    transfer learning;   

DOI  :  10.3390/bioengineering8030035

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown