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dc.contributor.authorTao, Shuailinen_US
dc.contributor.authorHu, Jinhaien_US
dc.contributor.authorGoh, Wang Lingen_US
dc.contributor.authorGao, Yuanen_US
dc.identifier.citationTao, S., Hu, J., Goh, W. L. & Gao, Y. (2024). Squeeze-excite fusion based multimodal neural network for sleep stage classification with flexible EEG/ECG signal acquisition circuit. 2024 IEEE International Symposium on Circuits and Systems (ISCAS).
dc.description.abstractThis paper presents a multimodal fusion strategy for sleep stage classification using polysomnography (PSG) with elec troencephalogram (EEG) and Electrocardiogram (ECG) data. The Squeeze-Excite (SE) Fusion mechanism is implemented to enhance the collaborative impact of EEG and ECG signals on neural network classification. To address the challenges of imbalance in the dataset, a balanced sampler is used. Improved feature extraction is achieved through Linear-frequency cepstrum coefficients (LFCC) applied to the EEG signal. A recurrent convolutional neural network (RCNN) reduces model parameters and optimizes architecture, while quantizing the network weight down to INT4 ensures hardware compatibility, especially for edge devices. Applying these methodologies to signals, this optimized approach achieves a significant validation accuracy of 77.6% with a compact 23.5KB weight memory size on the MIT-BIH dataset, covering six distinct classification categories.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.rights© 2024 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at
dc.subjectComputer and Information Scienceen_US
dc.titleSqueeze-excite fusion based multimodal neural network for sleep stage classification with flexible EEG/ECG signal acquisition circuiten_US
dc.typeConference Paperen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.conference2024 IEEE International Symposium on Circuits and Systems (ISCAS)en_US
dc.contributor.organizationInstitute of Microelectronics, A*STARen_US
dc.contributor.researchCentre for Integrated Circuits and Systemsen_US
dc.description.versionSubmitted/Accepted versionen_US
dc.subject.keywordsSleep stage classificationen_US
dc.subject.keywordsChannel attentionen_US
dc.subject.keywordsMultimodal neural networken_US
dc.subject.keywordsFlexible circuit and systemen_US
dc.description.acknowledgementThis work was supported by the Agency for Science, Technology and Research (A*STAR), Singapore under the Nanosystems at the Edge programme, grant No. A18A1b0055.en_US
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