An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification

Author:

Irfan Muhammad1ORCID,Siddiqa Hafza Ayesha1,Nahliis Abdelwahed2ORCID,Chen Chen3ORCID,Xu Yan4,Wang Laishuan5,Nawaz Anum6ORCID,Subasi Abdulhamit7ORCID,Westerlund Tomi6ORCID,Chen Wei1ORCID

Affiliation:

1. Center for Intelligent Medical Electronics (CIME), School of Information Science and Technology, Fudan University, Shanghai, China

2. Department of Circuits and Systems, School of Information Science and Technology, Fudan University, Shanghai, China

3. Human Phenome Institute, Fudan University, Shanghai, China

4. National Children’s Medical Center, Children’s Hospital of Fudan University, Shanghai, China

5. Children’s Hospital of Fudan University, Shanghai, China

6. Department of Computing, TIERS Group, University of Turku, Turku, Finland

7. Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland

Funder

Shanghai Municipal Science and Technology International Research and Development Collaboration Project

Opetushallitus

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis;Journal of Neuroscience Methods;2024-10

2. An Identification Method for Road Hypnosis Based on Human EEG Data;Sensors;2024-07-06

3. An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification;Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine;2024-07

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