Name
Title | ||
|---|---|---|
A review of automated sleep stage scoring based on physiological signals for the new millennia |
Abstract | ||
|---|---|---|
•Problem: Sleep stage scoring with Polysomnography is expensive and inconvenient for patients.•Position statement: Deep learning and the internet of health things can help to bring down the cost for sleep stage scoring while improving patient comfort.•Approach: We reviewed scientific work on sleep stage scoring systems that were based on: Heart Rate, Electrocardiogram, Electroencephalogram, Electrooculogram, and a combination of signals.•Result: Heart Rate signals are a good choice for patient led sleep stage scoring, because they provide the required information and the measurement setup is uncomplicated. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cmpb.2019.04.032 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Sleep stage,Deep learning,Internet of health things,Decision support systems | Computer vision,Computer science,Speech recognition,Information extraction,Redundancy (engineering),Sleep disorder,Artificial intelligence,Electroencephalography | Journal |
Volume | ISSN | Citations |
176 | 0169-2607 | 3 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Faust | 1 | 194 | 18.05 |
| Hajar Razaghi | 2 | 4 | 0.81 |
| Ragab Barika | 3 | 3 | 0.40 |
| Edward J. Ciaccio | 4 | 165 | 30.79 |
| Rajendra Acharya U | 5 | 4666 | 296.34 |