| Autor: |
Nuno Pombo, Bruno M. C. Silva, Andre Miguel Pinho, Nuno Garcia |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 8, Pp 200477-200485 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2020.3036024 |
| Popis: |
This article presents a study on the efficiency of implementing classifiers for the detection of sleep apnea moments based on a minute-to-minute Electrocardiogram (ECG) signal, detailing the comparison of the accuracy for different classifiers. At each ECG signal, a Sgolay filter was applied to extract the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR) and they were used for the training, testing and validation of the classifiers. The same features were extended in a second phase in order to understand if all the classified features were important. According to the results obtained, the best accuracy was 82.12%, with a sensitivity and a specificity of 88.41% and 72.29%, respectively. This study shows the importance of choosing the right classifier for a specific problem as well as choosing and using the best features for a better accuracy. These promising early-stage results may lead to complementary studies to improve the classifiers for a possible real-world application. The performance of the proposed model was compared with other approaches used for the detection of sleep apnea. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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