Name
Abstract | ||
|---|---|---|
Assessment of sleep can reveal healthy physiology and behaviour, which are essential to study diseases and treatment. The primary approaches to quantify sleep in animal models are using invasive methods that require implantation of electroencephalogram (EEG) and electromyogram (EMG) electrodes. Those methods are resource-intensive and less than ideal for high-throughput screening. Several studies proposed using video processing to monitor sleep. Those approaches require high quality videos and an optimal threshold value which can be sensitive to different experiment settings. In this paper, we present a trainable video-based approach that can alleviate those limitations. We have come up with a set of effective features at frame-level which are then put into a recurrent neural network to capture long-term temporal features. The result obtained is highly correlated with EEG/EMG-defined sleep. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683455 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Long Short Term Memory, sleep assessment, animal behavior recognition | Video processing,Pattern recognition,Computer science,Long short term memory,Recurrent neural network,Monitor sleep,Artificial intelligence,Electroencephalography | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Van Anh Le | 1 | 6 | 2.85 |
| Mitchell Kesler | 2 | 0 | 0.34 |
| Jong M. Rho | 3 | 0 | 0.68 |
| Ning Cheng | 4 | 1 | 6.76 |
| Kartikeya Murari | 5 | 145 | 18.78 |