Name
Title | ||
|---|---|---|
Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG. |
Abstract | ||
|---|---|---|
•IITNet extracts representative features at a sub-epoch level from raw single-channel EEG.•Intra- and inter-epoch temporal contexts are captured in multiple successive epochs.•Influence of the sequence length on performance was analyzed on SleepEDF, MASS, and SHHS.•Intra-epoch temporal context learning with a residual network enhanced sleep scoring performance.•Using last two-minute epochs can be reasonable for efficient and reliable automatic sleep scoring. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.bspc.2020.102037 | Biomedical Signal Processing and Control |
Keywords | DocType | Volume |
Deep learning,Classification,Single-channel EEG,Sleep scoring,Sequence,Temporal context,End-to-end | Journal | 61 |
ISSN | Citations | PageRank |
1746-8094 | 5 | 0.50 |
References | Authors | |
31 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hogeon Seo | 1 | 5 | 0.50 |
| Seunghyeok Back | 2 | 7 | 1.24 |
| Seongju Lee | 3 | 5 | 0.50 |
| Deokhwan Park | 4 | 5 | 0.50 |
| Tae-Hoon Kim | 5 | 459 | 53.02 |
| Kyoobin Lee | 6 | 52 | 8.36 |