Name
Abstract | ||
|---|---|---|
Current seizure detection systems rely on machine learning classifiers that are trained offline and subsequently require manual retraining to maintain high detection accuracy over long periods of time. For a true deploy-and-forget implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to the neural signal drifts over time. This work proposes SOUL: Stochastic-gradient-descent-based Online Unsupervised Logistic regression classifier, which provides continuous unsupervised online model updates that was initially trained with labels offline. SOUL was tested on two datasets, the CHB-MIT scalp EEG dataset, and a long (>250 hours) human ECoG dataset from the University of Melbourne. SOUL achieves an average cumulative sensitivity of 97.5% and 97.9% for the two datasets respectively, while maintaining <1.2 false alarms per day. When compared with state-of-the-art, a moderate sensitivity improvement of 1-3% is observed on the majority of subjects and a large sensitivity improvement of >12% is observed on three subjects with <1% impact on specificity. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9176122 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adelson Chua | 1 | 0 | 0.68 |
| Michael I. Jordan | 2 | 31220 | 3640.80 |
| Rikky Muller | 3 | 0 | 0.68 |