# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 2 Literature Review
### Deep-Learning Based Techniques
#### 2.2.2 ECG-Based Recognition Method  
Many recent studies used deep learning methods that applied to ECG biometrics. It's worth mentioning that CNNs are widely used in biometric recognition systems and provide satisfying results [29]. The authors of [30] used a multiresolution 1D-convolutional neural network (1D-CNN) in the ECG identification system. The ECG signal noise was removed, and blind segmentation was performed. Then, the wavelet transform was applied \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Year** & **Feature Extraction** & **Classifier** & **Dataset** & **Results and Accuracy** \\ \hline [4] & 2016 & MFCC & ANN & HST-11 & 96\% \\ \hline [12] & 2016 & AC/DCT & ANN & MIT-BIH & 97\% \\ \hline [13] & 2017 & Fiducial based features & ANN & & 92.45\% \\  & & KNN & ECG ID database & 92.72\% \\  & & SVM & & 93.70\% \\ \hline [14] & 2018 & Mean P-QRS-T Fragment + DWT & ANN & ECG-ID & 95\% \\  & & SVM & & MIT-BIH & \\ \hline [16] & 2012 & DCT & Random Forests & \begin{tabular}{c} MIT-BIH \\ PTB database \\ \end{tabular} & AVG ACC = 100\% \\ \hline [8] & 2019 & DCT & \begin{tabular}{c} Decision Tree \\ KNN \\ Random Forest \\ \end{tabular} & Not mentioned & \begin{tabular}{c} 93\% \\ 98\% \\ 99\% \\ \end{tabular} \\ \hline [17] & 2015 & DWT + HVR & Random Forests & \begin{tabular}{c} MITDB \\ NSRDB \\ ECG-ID \\ \end{tabular} & \
\begin{tabular}{c} 95.85\% \\ 100\% \\ 83.88\% \\ \end{tabular} \\ \hline [21] & 2020 & Cepstral coefficients+ ZCR+ Entropy & SVM &
\begin{tabular}{c} MIT-BIH \\ ECG-ID \\ \end{tabular} & 100\% \\ \hline \multirow{3}{*}{[22]} & \multirow{3}{*}{2019} & ANN & \multirow{3}{*}{ECG-ID} & \multirow{3}{*}{99.1379\% (LASSO with KNN)} \\  & & KNN & & \\ \cline{1-1}  & & OAA-SVM & & \\ \cline{1-1}  & & (With LASSO) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of machine learning techniques for human recognition. to the segments, and the phase difference caused by the blind segmentation was removed using autocorrelation. This approach achieved an average recognition rate of 93.5%. In another study [31], a CNN was proposed. After removing the noise from the signal, the QRS complex extraction was performed from the ECGs to feed a 1D CNN. The tested result shows a recognition rate of 100%. While the authors of [32] suggested a method that reduces the ECG signal length, since the ECG signal requires a long duration for the recognition of a person, He proposed a CNN for ECG classification based on the R-peak and transformed using continuous wavelet transformation (CWT). The method was evaluated on the PTB database and achieved 99.94% and 99.83% identification accuracy, respectively. Similarly, the authors of [33] used a short ECG signal around the R-peak that has been transformed into Continuous Wavelet Transformation (CWT) images, which are then used by the convolutional neural network (CNN) for the recognition process. The results show a classification accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for the ECG-ID multisession datasets. The authors of [34] used pre-configured models of CNN to evaluate ECG biometrics using different time-frequency representations like MFCC, spectrogram, log spectrogram, Mel spectrogram, and scalogram. They used the PTB-ECG and CU-ECG databases. The result shows that the MFCC has higher accuracy than other time-frequency representations. The authors of [35] proposed an approach for human authentication based on ECG using CNN. The proposed algorithm achieved an average accuracy of 97.92% on the MWMHIT database and 99.96% on the MIT-BIH database. Another study [36] proposed a system that uses CNN to identify a person using temporal frequency analysis like spectrograms. The system was tested on Fantasia and ECG-ID databases and achieved 99.42% and 94.23%, respectively. Similarly, the study [37] used CNN for biometric identification. The proposed method does not require R-peak detection. It was evaluated on the PTB database and achieved a recognition accuracy of 99.1%.