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List of the used abbreviations_
| Abbreviations | Descriptions |
|---|---|
| OSA | Obstructive sleep apnea |
| ECG | Electrocardiogram |
| EDR | ECG-Derived Respiration |
| AHI | Apnea-Hypopnea Index |
| HMM | Hidden Markov model |
| RUSBoost | Random under-sampling Boost |
| Adaboost | Adaptive boost |
| DWT | Discrete wavelet transform |
| TQWT | Tunable Q-factor wavelet transform |
| LDA/QDA | Linear/Quadratic Discriminant Analysis |
| SFFS | Sequential forward feature selection |
| SRDA | Spectral regression discriminant analysis |
| DNN/CNN | Deep/Convolutional neural network |
| DT classifier | Decision tree classifier |
| RBF | Radial basis function |
| SVM | Support vector machine |
| RLS | Recursive least squares |
| GS | Gram-Schmidt |
| STLF | Short-time load forecasting |
| DT-CWT | Dual-tree complex wavelet transform |
The comparison of the OSA detection results based on various methods_
| References | Feature extraction/s election method | Classifier | Results | ||
|---|---|---|---|---|---|
| ACC% | Sens% | Spec% | |||
| [1] Zarei 2018 | DWT+SFFS | SVM (RBF kernel) | 92.98 | 91.74 | 93.75 |
| [2] Song 2016 | HMM | HMM+SVM | 86.2 | 82.6 | 88.4 |
| [3] Hassan 2017 | TQWT | RUSBoost | 88.88 | 87.58 | 91.49 |
| [4] Gonzalez 2017 | Cepstrum+ Filter bank | QDA | 84.76 | 81.45 | 86.82 |
| [5] Hassan 2016 | Statistical and spectral | Bootstrap aggregating | 85.97 | 84.14 | 86.83 |
| [6] Hassan 2016 | Normal invers Gaussian modeling | AdaBoost | 87.33 | 81.99 | 90.72 |
| [7] Sharma 2016 | QRS features | LS-SVM (RBF kernel) | 83.8 | 79.5 | 88.4 |
| [8] Hilmisson 2018 | Frequency features | Statistical analysis | 93 | 100 | 81 |
| [9] Janbakhshi 2018 | Time domain feaures+PSD | SVM-KNN-NN-LD-QD | 90.9 | 89.6 | 91.8 |
| [10] Ma 2019 | Statistical features | Statistical analysis | 87 | 89 | 79 |
| [11] Nishad 2018 | Tunable-Q wavelet transform features | Random Forest | 92.78 | 93.91 | 90.95 |
| [12] Wang 2019 | RR-intervals | CNN (LeNet-5) | 92.3 | 90.9 | 100 |
| [13] Singh 2019 | Time-frequency Scalogram features | CNN (AlexNet) | 86.22 | 90 | 100 |
| [14] Urtnasan 2018 | RR-intervals | CNN | 96 | 96 | 96 |
| [15] Wang 2018 | RR-intervals | CNN | 97.8 | 100 | 93 |
| [16] Sharma 2019 | Fuzzy-entropy (FUEN) and the Log of signal-energy (LOEN) | KNN-DT-SVM | 90.87 | 92.43 | 88.33 |
| [17] Avci 2015 | DWT+PCA | Random forest | 92–98 | - | - |
| [18] Rachim 2014 | DWT+PCA | SVM | 94.3 | 92.65 | 92.2 |
| Proposed method | DT-CWT+SRDA | Hybrid “k-means, RLS” RBF | 95.62 | 96.37 | 96 |
List of non-linear features that are extracted from the DT-CWT coefficients in this paper_
| Features | Description |
|---|---|
| FE | Fuzzy Entropy |
| ApEn | Approximate Entropy |
| IQR | Interquartile Range |
| RP | Recurrence Plot |
| SD1, SD2, SD1/SD2 | Poincare Plot |