Table 2 Summary of intelligent ML-based SAA using stand-alone ECG
Refs., Year | Database | Signal for FE, Segment length | Method description | ICF | FS | FV | Limitation | Key Findings |
|---|---|---|---|---|---|---|---|---|
[20], 2007 | CUDB, VFDB, AHA | Stand-alone ECG, 10 s | Use of 10 ICFs as the input of Linear discriminant analysis to select an FFC of 4 features. | 10 | Linear discriminant analysis embedding FS | NA | Limited number of ICFs. | FFC of 4 features. |
No validation. | Prediction of success of defibrillation. | |||||||
[25], 2011 | MITDB | Stand-alone ECG, QRS comlex of 200 points | Performance comparison of KNN, neural networks and ensemble based methods. | 7 | NA | NA | Limited database. | Better performance of DECORATE model than others. |
Limited number of ICFs. | ||||||||
No FS and FV | ||||||||
[21], 2014 | CUDB, VFDB, MITDB | Stand-alone ECG, 8 s | SBFS including SVM and boots trapping to select an FFC of 7 features on training data. | 13 | SBFS using SVM and boostrapping | NA | Limited number of ICFs. | FFC of 9 features. |
Performance of SVM using a FFC on testing data. | No validation. | |||||||
[22], 2012 | CUDB, VFDB, MITDB | Extraction of 11 Vleak features. | 11 | NA | NA | Limited number of ICFs. | FFC of 11 features and SVM. | |
Comparison performance of SVM and VLeak threshold for VF/non-VF and shock/non-shock. | No FS and FV. | Better performance of SVM than Vleak. | ||||||
[23], 2014 | CUDB | Use of Hilbert transforms for peak extraction, phase space reconstruction, time domain analysis. | 15 | NEWFM embedding FS. | NA | Limited database. | FFC of 11 features. | |
NEWFM embedding FS to select an FFC of 11 features. | No separated data for FS and testing. | |||||||
No validation. | ||||||||
[29], 2016 | CUDB, VFDB, AHA, OHCA | SBFS including 2 ML classifiers and bootstrapping to select 2 CFCs. | 30 | SBFS using Logistic regression, Boosting and boostrapping. | Bootst-rapping | Lower validation performance of CFCs than all ICFs. | Large number of ICFs. | |
Validation of CFCs and a combination of all ICFs using 5 ML classifiers and bootstrapping. | OHCA data requires two times more features than public data. | |||||||
[30], 2017 | CUDB, VFDB, MITDB | SVM to rank 26 ICFs and selection of 19 features. | 26 | Feature ranking with SVM | Record-based data division. | No validation for all ICFs. | FFC of 3 features | |
Validation of every combination of 19 features using SVM and random data division. | Database-based data division. | |||||||
[24], 2018 | CUDB, VFDB, MITDB, AFDB | Algorithm design for classification of VF/non-VF, Atrial fibrillation/non | 6 | NA | NA | Limited number of ICFs. | Effective features computed from time-delay algorithm. | |
-Atrial fibrillation, premature ventricular contraction/non-premature ventricular contraction, and sinus arrhythmia. | No FS and FV. | |||||||
SVM and Bayer decision tree for VF/non-VF classification. | ||||||||
[31], 2016 | CUDB, VFDB, MITDB | Use of RF and 10folds CV to validate the combination of all ICFs for different window lengths. | 17 | NA | 10-folds CV | No FS | Best performance for overlapping 8 s-segment. | |
[32], 2014 | CUDB, VFDB, MITDB | Stand-alone ECG, 5 s | GA based feature ranking. | 14 | GA | five-folds CV | -Limited number of ICFs. | FFC of 2 features. |
Performance investigation of every combination of 9 features using SVM. | No validation for all ICFs. | |||||||
Validation of 9 combinations using five-folds CV and SVM. | ||||||||
[33], 2018 | CUDB, VFDB | GA based feature ranking for selection of 7 good features. | 11 | GA | Five-folds CV | Limited number of ICFs. | FFC of 4 features. | |
Performance estimation of SVM using every feature combination of good features. | Only 1 classifier | |||||||
Validation performance of SVM using 6 combinations with five-folds CV. | Similar method of [15] | |||||||
[34], 2018 | CUDB, VFDB | C4.5 for classification of normal, VF, and VT segments | 13 | GRAE | 20-folds CV | Highest performance of all ICFs | Identification of confidence factor value of C4.5 | |
Feature ranking using gain ratio attribute evaluation. | ||||||||
Investigation of different confidence factor for C4.5 | ||||||||
[37], 2021 | MITDB, CUDB,VFDB | Application of SVM and AdaBoost for the FS based differential evolution algorithm and classification of VF and non-VF rhythms. | 17 | Differential evolution algorithm | 10-folds CV | Only SVM. | Effective AdaBoost for data weight assignment to improve SVM classification performance. | |
Extraction of 17 conventional features and selection of 3 as the optimal feature subset | Limited number of ICFs. | |||||||
No validation for all ICFs. | ||||||||
[35], 2012 | VFDB, AHA | Stand-alone ECG, 1.024 s | Extraction of temporal, spectral, and time-frequency features. | 37 | SVM-bootstrap resampling SVM-recursive feature elimination Filter methods | 5-folds CV | Performance analysis based on 1 ML classifier. | Highest performance of FFC of 3 features. |
Comparison of SVM-bootstrap resampling, SVM-recursive feature elimination and filter methods. | Only 1 s-segment | Efficient SVM-bootstrap resampling. | ||||||
[26], 2012 | VFDB,AHA | Extraction of temporal, spectral, and time-frequency features. | 27 | Bootstrap resampling | NA | Performance analysis based on a ML classifier. | Self organizing map using an FFC of 11 features. | |
Selection of 11 features using bootstrap resampling based feature selection. | ||||||||
Self organising map for classification. | ||||||||
[36], 2019 | CUDB, VFDB, NSRDB | Stand-alone ECG, 3 s | Investigation of 47 time domain and wavelet features. Selection of 25 by FS. | 47 | Gaussian GA | 3-folds CV | Investigation of a classifier. | 17 wavelet features (out of 25) show the significant efficiency of wavelet method. |
Detection of VF/VT and normal ECG segment by the first SVM classifier. | Validation on only a database. | Practical application for AED processor. | ||||||
Discrimination between VF and VT by the second SVM classifier. | Low average CV performance | Reletive short of segment length. | ||||||
Hardware implementation of the proposed algorithm for the AED. |