Citation

Tawsif Khan, Chy. Mohammed and Ab Aziz, Nor Azlina and Emerson Raja, Joseph (2021) Emotion Recognition using ECG Raw Data with Deep Learning. In: 2nd FET PG Engineering Colloquium Proceedings 2021, 1-15 Dec. 2021, Online Conference. (Unpublished)

Text 01 Tawsif.pdf Restricted to Repository staff only Download (67kB)

Abstract

WESAD dataset is used which is consists of ECG data of 15 subjects with two targets (stressed vs non-stressed). The deep learning model is designed with 33 layers. As a real-time emotion recognition, a window with length of 17,920 of raw ECG data was used without any feature extraction process to perform each decision. In the hidden layers, RELU activation function and sigmoid function were used in hidden layers and output layer respectively.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	ECG, Deep Learning, Emotion, Stress, Deep learning (Machine learning)
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	25 Jan 2022 07:37
Last Modified:	20 Feb 2023 07:53
URII:	http://shdl.mmu.edu.my/id/eprint/9872

Downloads

Edit (login required)