Name
Title | ||
|---|---|---|
Apnea-Hypopnea Index Prediction Using Electrocardiogram Acquired During the Sleep-Onset Period. |
Abstract | ||
|---|---|---|
The most widely used methods for predicting obstructive sleep apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep apnea screening, this study aimed to devise a new predictor of apnea-hypopnea index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep apnea during the sleep-o... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TBME.2016.2554138 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Sleep apnea,Indexes,Electrocardiography,Heart rate,Electronic mail,Medical diagnostic imaging | Obstructive sleep apnea,Sleep onset,Sleep apnea,Regression analysis,Anesthesia,Artificial intelligence,Heart rate,Apnea–hypopnea index,Medicine,Polysomnography,Coefficient of variation,Computer vision,Internal medicine,Cardiology | Journal |
Volume | Issue | ISSN |
64 | 2 | 0018-9294 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Da Woon Jung | 1 | 1 | 1.09 |
| Su Hwan Hwang | 2 | 11 | 2.96 |
| Yu Jin Lee | 3 | 0 | 1.69 |
| Do-Un Jeong | 4 | 0 | 0.34 |
| Kwang Suk Park | 5 | 266 | 46.43 |