|
[7], 2018
|
CUDB, VFDB, MITDB
|
Stand-alone ECG, 2 s
|
The 11-layers CNN for classification of SH/NSH 2 s-ECG segment.
|
NA
|
NA
|
10-folds CV
|
Only one CNN structure.
|
Simple full CNN.
|
|
Validation of full CNN with 10-fold CV.
|
Time-consuming for validation of the full CNN.
|
Less complexity due to no FS and FV.
|
| | |
Relative short of segment length.
|
|
[8], 2018
|
CUDB, VFDB
|
ECG segment, NSH signal, SH signal, using MVMD, 8 s
|
NSH, SH signals generated by MVMD.
|
NA
|
NA
|
5-folds CV
|
Time-consuming for selection of CNNE.
|
Improvement of LF quality due to multiple channels.
|
|
Use of ECG segment, NSH and SH signals as input channels of CNN
|
Improvement of final performance due to secondary training of ML classifier.
|
|
Grid search with nested 5-fold CV to select best structure and parameters of CNNE using ML classifiers.
|
No need of FE and FS.
|
|
Validation of feature vector extracted by CNNE with different ML classifiers.
| |
|
[46], 2020
|
CUDB, VFDB
|
Modes using FFREWT, 8 s
|
FFREWT for ECG segment decomposition into 6 modes.
|
NA
|
NA
|
10-folds CV
|
pre-selected CNN, structure.
|
High performance. Effective FFREWT for generation of different modes containing VF, VT, normal components of ECG.
|
|
First 5 modes used as the input of CNN for detection of SH and NSH ECG segment, VF and VT rhythms
|
|
[47], 2019
|
CUDB, VFDB, AHADB
|
Stand-alone ECG, 4 s
|
1D parallel CNN, LSTM and ANN for classification of 4 s-ECG segment.
|
NA
|
NA
|
NA
|
pre-selected CNN, LSTM, ANN structure.
|
High performance.
|
|
No validation.
|
Multiple DLs for deep feature extraction.
|
| |
Relative short of segment length.
|
|
[48], 2020
|
EMS
|
Stand-alone patient’s ECG, 4 s
|
Fully CNN architecture and ResNet CNN model
|
NA
|
NA
|
10-folds CV
|
pre-selected CNN structure.
|
High performance for 4 s-ECG segment.
|
|
Classification of SH/NSH for different ECG segment length.
|
Relative short of segment length.
|
|
Validation with 10-fold CV.
| |
|
[49], 2020
|
CUDB, VFDB, MITDB, OHCA1,OHCA2
|
Stand-alone ECG, 5 s
|
Random search based method for hyper-parameters of optimal deep CNN models using number of sequential CNN blocks, number of filters, kernel sizes.
|
NA
|
NA
|
NA
|
No validation.
|
Productive method for selection of a deep CNN structure with optimal hyper-parameters.
|
|
Median values to rank the optimal deep CNN models trained with various learning rates and ECG segment lengths to select the best deep CNN model.
|
Time-consuming for hyperparameter optimization
|
Model ability related to SH/NSH classification is depended largely on hyper-parameters.
|
|
[50], 2021
|
CUDB, VFDB
|
Stand-alone ECG, 5 s
|
DNN using a feature set extracted from ECG segments pre-processed by DWT, EMD, VMD.
|
24
|
NA
|
NA
|
No validation.
|
Relative short of segment length
|
|
Comparison to various ML classifiers
|
pre-selected DNN
|
Effective decomposition techniques for data processing.
|
|
[51], 2021
|
CUDB, VFDB, MITDB, AHADB
|
Time-frequency maps, 3 s,5 s,8 s,10 s
|
Conversion of ECG segments into time-frequency maps using CWT.
|
NA
|
NA
|
10-folds CV
|
Time-consuming for selection of an optimal 2D CNN structure
|
Relative short of segment length
|
|
Investigation of eight 2D CNN structures.
|
Productive transformation method using CWT.
|
|
Consideration of different ECG segment lengths.
| |
