A semi-supervised deep learning algorithm for abnormal EEG identification

2019/03/19 by Subhrajit Roy, Roy, Subhrajit, Kiran Kate +3
Computer Science · Engineering · Mathematics · Medicine · Neuroscience · #Blind Source Separation Techniques #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML

paper · pdf · doi:10.48550/arxiv.1903.07822

Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract

openalex publication_date 2019/03/19 · openalex created_date 2019/03/22 · arxiv created 2019/11/06 · arxiv updated 2019/11/11 · openalex updated_date 2026/07/28

Abstract

Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists we try to aid. This paper proposes a semi-supervised learning workflow that can not only extract meaningful information from large unlabeled EEG datasets but also make predictions with minimal supervision, using labeled datasets as small as 5 examples.

Citations

• Variational Inference: A Review for Statisticians

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