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Table 5 Summary of intelligent DL-based SAA

From: A review of progress and an advanced method for shock advice algorithms in automated external defibrillators

Refs., Year Database Signal for FE, Segment length Method description ICF FS FV Limitation Key Findings
[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.