Name
Title | ||
|---|---|---|
Uncovering Low-Dimensional Structure In High-Dimensional Representations Of Long-Term Recordings In People With Epilepsy |
Abstract | ||
|---|---|---|
Effective representations of recordings of epileptic activity for seizure prediction are high-dimensional, which prevents their visualization. Here we introduce and evaluate methods to find low-dimensional (2D or 3D) descriptors of these high-dimensional representations, which are amenable for visualization. Once low-dimensional descriptors are found, it is useful to identify structure in them. We evaluate clustering algorithms to automatically identify this structure. In addition, typical recordings of epileptic activity are long, extending for several days or weeks. We present and assess extensions of the previous methods to handle large datasets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856421 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Pattern recognition,Computer science,Visualization,Epilepsy,Artificial intelligence,Cluster analysis,Principal component analysis | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joaquin Rapela | 1 | 0 | 0.34 |
| Timothée Proix | 2 | 0 | 0.68 |
| Dmitrii Todorov | 3 | 0 | 0.68 |
| Wilson Truccolo | 4 | 64 | 12.78 |