# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 2 Literature Review
### Machine-Learning Based Techniques
#### 2.1.3 ECG-Based Recognition Method  
Many studies have proposed recognition methods based on ECG signals. The authors of [12] suggested an ECG recognition method. They reduced the ECG noise. A non-fiducial technique based on autocorrelation and discrete cosine transformation (AC/DCT) was used to extract features. The data were collected from the MIT-BIH Arrhythmia Database. The result showed a classification accuracy of 97% using ANN. In another study, the authors [13] proposed an ECG recognition method. They used a Cascaded FIR Filter configuration for noise removal, and a Frequency-Time-based ap-preach was used for characteristic wave detection. The data were obtained from the MIT-BIH ECG ID database. The features were extracted using R-Peak position normalization. For classification, they use three classifiers: ANN, K-NN, and SVM. The results show that the SVM classifier provides 93.709% classification accuracy. Similarly, [14] proposed a human identification method based on ECG. For classification, they used three classifiers: ANN, KNN, and SVM. The method was evaluated on the ECG-ID and MIT-BIH databases. Unlike other studies that used ECG signals for personal identification under the condition of rest, the authors of [15] focus on using ECG signals for personal identification when the heart rate is increased through exercise. For analyzing the ECG signal, we used the root mean square (RMS) value, nonlinear Lyapunov exponent, and correlation dimension. For recognizing identity, it used a support vector machine (SVM) and achieved over 80% recognition accuracy. The authors of [16] proposed a method that used the discrete wavelet transform (DWT) for feature extraction and random forests for authentication. Results show that the system can achieve 100% recognition accuracy. Another study [17] proposed an ECG-based identification system using the discrete wavelet transform (DWT) of the cardiac cycle and heart rate variability (HRV) for feature extraction and Random Forests for classification. The system was evaluated on three databases and achieved an accuracy of 95.85%, 100%, and 83.88%. The authors of [18] proposed a method for an authentication system based on ECG signals. First, the raw ECG signal is denoised using empirical mode decomposition (EMD). In the next step, features are extracted. Finally, selected features were classified using various classification methods such as Support Vector Machines (SVM), K-nearest neighbor (KNN), and Decision Tree (DT). Based on the evaluation, the SVM with a cubic kernel achieves a classification accuracy rate of 98.7%. Another study [19] proposed an ECG-based approach for authentication. The method consists of removing noise from the signal by using wavelet decomposition. An empirical mode decomposition (EMD) is used to decompose the denoised into several intrinsic mode functions (IMFs). Then, the features are extracted. The ECG signals are finally classified using SVM. The evaluation result shows that the cubic SVM has the highest accuracy of 98.4%. The authors of [20] compared three different Machine Learning algorithms: KNN, SVM, and Gaussian Naive Bayes (GNB). The result shows an accuracy of over 90%. The authors of [21] used three types of features, namely cepstral coefficients, the ZCR, and entropy, to identify individuals. Then, the classification was performed using various types of SVM kernels. The evaluation performed on the MIT-BIH arrhythmia and ECG-ID databases shows that the proposed method can achieve an identification accuracy of 100%. Another study [22] used a statistical learning method (LASSO) to select the appropriate features for classification. Then, we used some machine learning algorithms such as ANN, SVM, and KNN for classification. The experimental results show that LASSO with a KNN classifier achieves a recognition accuracy of 99.1379%. This has been observed for the proposed method of LASSO with the KNN classifier.