# 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
While the authors of [38] proposed cascaded CNNs that consist of two CNNs, called F-CNN for feature extraction and M-CNN for identification, the approach was evaluated using different databases and achieved an average recognition of 94.3%. The method described in [39] used a 2-D coupling image of the ECG signal. CNN, which specializes in processing images, is used to process the 2-D coupling image generated from three periods of the ECG signal. The system was tested on the PTB and MIT-BIH datasets and achieved classification accuracy of 98.45% and 99.2%, respectively. The authors of [40] proposed a method that uses the image of the spectral correlation of the ECG as the system's input to various CNN networks. For classification, they used two models of a 2D convolutional neural network. The system was evaluated on different ECG databases. Similarly, [41] proposed a two-dimensional convolutional neural network (2D CNN). Firstly, ACDCT features and cepstral properties were extracted from ECG signals. Then, transform these features from 1D to 2D. The approach was evaluated in the PTB database and achieved an accuracy of 88.57%. The method described in [42] used short-time Fourier transform (STFT) and generalized Morse wavelets (CWT) for feature extraction and 2D-CNN for classification. The model was evaluated on eight databases. The SFTF system achieved an average accuracy of 97.85%, while the CWT achieved an average accuracy of 97.5%. The author of [43] proposed an approach for biometric identification based on ECG signals obtained from fingers. Time series are converted into 2D images, and then CNN is used for classification. The authors of [44] Proposed a person identification framework based on LSTM using ECG signals. After removing the noise from ECG signals, the ECG signal is windowed to various segments in the windowing module. After that, these segments have been fed to an LSTM network to learn the underlying representation and then classify it into the appropriate person categories. The model was evaluated on four databases and achieved an accuracy of 97.3% for the PTB database and 79.37% for CYBHi. To convert ECG signals to images, they used time-frequency representations such as the short-time Fourier transform (STFT), scalogram, Fourier synchro-squeezed transform (FSST), and wavelet synchro-squeezedtransform (WSST). For evaluation, a CU-ECG database is used. Another study [45] proposed bidirectional long-short-term memory (LSTM)-based deep recurrent neural networks (DRNN) for ECG-based classification. They evaluated the method using the NSRDB and MITDB databases and achieved 100% and 99.8% classification accuracy, respectively. The method proposed in [46] used RNN for ECG-based biometric classification. ECG data were fed directly to RNN without feature extraction. The proposed method was evaluated on the ECG-ID and MIT-BIH Arrhythmia (MITDB) datasets and achieved a classification accuracy of 100%. Another study [47] proposed a Bidirectional Gated Recurrent Unit (BGRU) model based on deep RNN networks in bidirectional training with LSTM and GRU cell units. The approach was evaluated with the ECG-ID Database and MIT-BIH and achieved classification accuracy of 98.60% and 98.40%, respectively. The authors of [48] proposed a parallel, multi-scale, one-dimensional residual network based on three kernels using center and margin loss during the training. The proposed method was evaluated in three databases and achieved 98.24%, 100%, and 95.99% classification accuracy. Another study [49] proposed a method using residual depth-wise separable convolutional neural networks (RDSCNN). They used [PERSON]'s method for ECG beat detection, and the RRDSCNN algorithm was used for classification. The method was evaluated in the ECG-ID and MIT-BIH databases and achieved classification accuracy of 98.89% and 97.92%, respectively. The authors of [50] proposed strategies for Electrocardiogram (ECG)-based identification. This paper used a DAE to learn the features of ECG signals automatically and a DNN for ECG identification.