Self-supervised ECG Representation Learning for Affective Computing
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Authors
Sarkar, Pritam
Date
Type
thesis
Language
eng
Keyword
Deep Learning , Machine Learning , Self-supervised Learning
Alternative Title
Abstract
This work investigates the use of self-supervised learning for ECG-based affective computing. We propose a novel self-supervised ECG representation learning framework to address the limitations of fully-supervised learning. Our proposed framework is developed using four popular ECG-affect datasets that contain a wide variety of emotional attributes such as arousal, valence, stress, and others, collected in different experimental settings. Our proposed solution achieves promising results and sets new state-of-the-art in classifying affect in all four datasets. We present interesting insights regarding our proposed framework and analyze the relationship between the self-supervised tasks and emotion recognition. Further, we explore the concept of self-supervised affective computing and utilize the framework in an applied setting. To this end, we collect ECG and affect data from medical practitioners during a trauma simulation study, and utilize our proposed self-supervised framework for classification of cognitive load and levels of expertise, achieving great results and outperform fully supervised solutions.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-ShareAlike 3.0 United States
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-ShareAlike 3.0 United States