Name
Title | ||
|---|---|---|
Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals. |
Abstract | ||
|---|---|---|
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BSN.2013.6575488 | BSN |
Keywords | Field | DocType |
accuracy,biomedical engineering,testing,accelerometers,sensors,acceleration | Sleep apnea,Computer science,Accelerometer,Speech recognition,Apnea,Sleep disorder,Acceleration,Breathing,Discrete wavelet transform,Electrocardiography | Conference |
ISSN | ISBN | Citations |
2325-1425 | 978-1-4799-0331-3 | 1 |
PageRank | References | Authors |
0.36 | 4 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin T. Sweeney | 1 | 79 | 5.12 |
| Edmond Mitchell | 2 | 45 | 4.60 |
| Jennifer Gaughran | 3 | 1 | 0.36 |
| Thomas Kane | 4 | 3 | 0.74 |
| Richard Costello | 5 | 1 | 0.36 |
| Shirley Coyle | 6 | 70 | 9.05 |
| Noel E. O'Connor | 7 | 2137 | 223.20 |
| Diamond Dermot | 8 | 154 | 25.80 |