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Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier.
- Source :
-
Entropy . Mar2023, Vol. 25 Issue 3, p399. 17p. - Publication Year :
- 2023
-
Abstract
- Obstructive Sleep Apnea (OSA) is a common sleep-breathing disorder that highly reduces the quality of human life. The most powerful method for the detection and classification of sleep apnea is the Polysomnogram. However, this method is time-consuming and cost-inefficient. Therefore, several methods focus on using electrocardiogram (ECG) signals to detect sleep apnea. This paper proposed a novel automated approach to detect and classify apneic events from single-lead ECG signals. Wavelet Scattering Transformation (WST) was applied to the ECG signals to decompose the signal into smaller segments. Then, a set of features, including higher-order statistics and entropy-based features, was extracted from the WST coefficients to formulate a search space. The obtained features were fed to a random forest classifier to classify the ECG segments. The experiment was validated using the 10-fold and hold-out cross-validation methods, which resulted in an accuracy of 91.65 % and 90.35 % , respectively. The findings were compared with different classifiers to show the significance of the proposed approach. The proposed approach achieved better performance measures than most of the existing methodologies. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RANDOM forest algorithms
*SLEEP apnea syndromes
*DROWSINESS
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 25
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 162812557
- Full Text :
- https://doi.org/10.3390/e25030399