Refs., Year | Database | Signal for FE, Segment length | Method description | ICF | FS | FV | Limitation | Key Findings |
---|---|---|---|---|---|---|---|---|
[16], 2016 | CUDB, MITDB, AHA | Subsignals using DWT, 3Â s | Use of DWT for reconstruction of subsignals. | 1 | NA | Five-folds CV | Insignificant improvement for classification performance. | Only a feature and SVM classifier. |
Calculation of the number of samples which is larger or smaller than positive or negative thresholds during 1Â s segment. | Time consumed for FE may be over segment length of 3Â s. | Relative short of segment length. | ||||||
Use of average numbers of samples as a feature for SVM classifier. | Â | Â | ||||||
[27], 2017 | MITDB | Subsignals using DWT, 5.7Â s | 5 levels of wavelet coefficients using DWT. | 20 | NA | NA | Limited database. | Peak extraction from wavelet coefficients. |
Peak extraction from wavelet coefficients, plotted in 3D PRS. | No FS and validation. | |||||||
NEWFM classifier using 20 features considered as distances between origin of coordinates axis and peaks. | Â | |||||||
[40], 2017 | CUDB, VFDB, MITDB | Subsignals using DWT, 10Â s | DWT with 4 levels-decomposition. | 31 | SFFS | Five-folds CV | Only 1 classifier. | FFC of 10 features. |
Feature extracted from wavelet coefficients. | No validation performance for all ICFs. | The best ranking method of ReliefF. | ||||||
SFFS to select 14 features. | Â | Â | ||||||
Feature ranking using 6 methods for set of 14 features. | Â | Â | ||||||
KNN classifier using different sets of features of 6 ranking methods. | Â | Â | ||||||
[38], 2019 | CUDB VFDB, MITDB | Subsignals using DWT, 5Â s | Performance comparision of C4.5 and SVM for detection of VF, VT. | 24 | GRAE | 10-folds CV | Highest performance of all ICF. | Generation of signals concentration on VT and VF components based on DWT. |
Using DWT as low-pass and high-pass filters for generation of alternative signals. | Ineffective FS. | |||||||
Features extracted from alternative signals. | Â | |||||||
[39], 2016 | CUDB, VFDB, MITDB | Subsignal using wavelet decomposition, 2Â s | Analysis on wavelet decomposition to design an optimal low-pass filter showing a minimum stopband ripple energy. | 12 | NA | 10-folds CV | Limited number of ICFs. | Selection of six subsignals based on orthogonal conditions. |
No FS. | Productive SVM for SH rhythm detection. | |||||||
 | Relative short of segment length | |||||||
[41], 2016 | CUDB, VFDB, MITDB | Modes using VMD, 5Â s | 5 modes using VMD. | 9 | FS based feature scoring | Five-folds CV | Limited number of ICFs. | Modes using VMD for FE. |
FE from first 3 modes. | Hand-picked data. | FFC of 7 features. | ||||||
The FS based feature scoring to select an FFC of 7 features. | Random reconstruction of modes. | Â | ||||||
Validation of the FFC using RF and five-folds CV. | Â | Â | ||||||
[43], 2017 | AFDB MITDB, NSRDB | Modes using VMD, 8Â s | Decomposition of ECG into 5 modes. | 20 | NA | Five-folds CV | No FS. | Effective entropy features. |
Sample entropy and distribution entropy of modes. | Hand-picked data. | High performance of SVM with KBF kernel among others. | ||||||
Performance of 2Â ML classifiers for normal, AF, and VF scenario. | Random generation of modes. | Â | ||||||
 | Limited number of ICFs |  | ||||||
[42], 2018 | CUDB, VFDB, MITDB | Modes using adaptive VMD, 5Â s | 5 modes using adaptive VMD. | 30 | NA | 10-folds CV | No FS. | Optimal parameters for adaptive VMD. |
10-folds CV for Boosted CART using all ICFs. | Simple selection of VMD parameters. | |||||||
[28], 2018 | CUDB, VFDB, | Modes using dimensional Taylor Fourier transform, 8Â s | Decomposition of ECG segment into oscillatory modes using dimensional Taylor Fourier transform. | 20 | NA | NA | Low performance. | New diagnostic features of magnitude and phase differences using dimensional Taylor Fourier transform. |
20-dimension feature vector based on magnitude and phase differences. | No FS and FV. | |||||||
LSSVM classifier for detection of shock/non-shock, VT/VF, and VF/non-VF. | Only 1 classifier. | |||||||
[17], 2009 | MITDB | Intrinsic mode functions using EMD, 7Â s | Use of intrinsic mode function with EMD. | 2 | NA | NA | No validation. | Orthogonality of IMFs as the features. |
Calculation of 2 angles between first 3 IMFs for Bayer decision theory. | Limited database | |||||||
[18], 2017 | VFDB AHA | Image of time-frequency, 150 ms | Construction of time-frequency image. | 1 | NA | NA | Only 1 feature. | Algorithm design for multiple classification using different binary ML classifiers. |
Performance comparison of different ML classifiers for classification of normal, VF, VT, and other rhythms. | No validation. | |||||||
 | Complexity due to 3 ML classifiers for multiple classification | |||||||
[19], 2018 | VFDB AHA | Time-frequency representation image, 1.2Â s | Extraction of image using Hilbert transform and Time-frequency representation techniques. | 1 | NA | Five-folds CV | Only 1 feature. | Effective feature of TFRI image. |
Use of multiple ML classifiers to detect normal, VF, VT, other rhythms. | Increase in complexity due to binary algorithms for multiple classification | Hierarchical topology of 3Â ML classifiers. |