Table 3.
Various studies conducted on the automated detection of microarousal regions in PSG signals using traditional machine learning methods.
| Author (Year) [Reference] | Database | Data Preprocessing | Machine Learning Model | Results |
|---|---|---|---|---|
| Huupponen et al. (1996) [43] | Local dataset | FFT, average power | MLP | Accuracy = 41% |
| Patanerli et al. (1999) [63] | Naya University | Wavelet transform, moving average, filter | SAS software; STEPDISC program | Sensitivity = 88.1%, Selectivity = 74.5% |
| Gouveia et al. (2003) [39] | Local dataset | FFT, frequency analysis | A set of scoring rules | Detection rate = 70% |
| Cho et al. (2005) [41] | South Korea’s Asan Medical Center | Filtering, power spectrum, FFT | SVM | Sensitivity = 75.26%, Specificity = 93.08% |
| Agarwal et al. (2006) [37] | Local dataset (two patients) | Second-order adaptive filter, frequency, MAA, etc. | A set of decisional rules | Sensitivity = 76.15% |
| David et al. (2006) [36] | National Institutes of Health (NIH) Sleep Disorders Research Plan | 1. Bi-directional recursive filtering, 2. peak detection 3. relative trough position |
Passive ballistocardiograph-based system | Sensitivity = 77.3%, Specificity = 96.2% |
| Shmiel et al. (2009) [42] | Aviv’s Assuta Medical Center | FFT, critical points, etc. | Sequential pattern discovery field | Sensitivity = 75.2%, positive predictive value = 76.5% |
| Foussier et al. (2013) [38] | Self-bulit database | HRV, MD, 72 features | Linear mixed mode | |
| Espiritu et al. (2015) [40] | Texas State Sleep Center | Savitzky-Golay filter, energy power/entropy, zero-crossing rate, etc. |
Decision tree | Accuracy = 81.63% |
| Shahrbabaki et al. (2015) [44] | Self-bulit database (6 male, 3 female) |
Butterworth filter, Welch’s algorithm, 32 features |
KNN | Accuracy = 93.6% |
| Wallant et al. (2016) [45] | Self-bulit database (35 healthy volunteers) | PSD, filtering data, segmentation, maximal amplitude, and slope | Adapted thresholds | Sensitivity = 83% |
| Subramanian et al. (2018) [65] | PhysioNet 2018 | 28 features | GLM, RF | Highest AUROC = 0.847, highest AUPRC = 0.630 |
| Ugur et al. (2019) [66] | SHHS | CWT | SVM | Accuracy = 98.2%, positive predictive value = 97.93% |
| Liu et al. (2020) [64] | PhysioNet 2018 | ICA, double density DWT algorithm, FIR filter | CNN with RF | AUPRC = 0.552 |
MLP = multilayer perceptron neural network; SVM = support vector machine; MAA = maximum absolute amplitude; HRV = heart rate variability; RF = random forest; SCL = skin conductance level; GLM = generalized linear model; CWT = continuous wavelet transforms; ICA = independent component correlation algorithm; DWT = discrete wavelet transformation; AUROC = area under the receiver operating characteristic curve; AUPRC = area under the precision-recall curve.