[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.
| | |