# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 4 Results and discussions
### State-of-the-Art Comparisons  
The proposed approach's classification result is compared with other state-of-the-art works that tested their systems using the ECG-ID database. The comparison is shown in Table 11.  
We discussed cardiac biometrics using deep learning, focusing on electrocardiogram (ECG) signals. The paper proposed an ensemble approach, combining VGG16 and LSTM architectures, for feature extraction from 2D spectrogram images. Based on their cardiac biometric data, the system achieved impressive accuracy and performance in recognizing humans. The proposed system holds the potential for secure authentication in various applications. This section presented and discussed the result of our classification model and compared the effect with different models and other state-of-the-art works. The comparison shows that our model achieved the best result. Compared to other systems, the proposed approach based on VGG-16-LSTM and LightGBM achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. Table 12 presents a comparison of different state-of-the-art preprocessing steps. Various classifiers were evaluated for their performance metrics, including accuracy, precision, recall, and F1-score. The works include methods like DWT combined with S-AE and 1D-ECG [1], achieving an accuracy of 85.50% with corresponding precision, recall, and F1-score values. Another approach employed PCANet, MaxFusion, and SVM [21], resulting in an accuracy of 83.11% and comparable precision, recall, and F1-score. A CNN-based method [51] achieved an accuracy of 83.22% but showed slightly lower precision and recall. Another \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
[1] & DWT and S-AE and 1D-ECG & 82.3\% & 84.3\% & 83.3\% & 0.82 \\
[21] & PCANet and MaxFusion and SVM & 85.70\% & 86.20\% & 83.30\% & 0.85 \\
[49] & CNN & 88.63\% & 84.13\% & 86.60\% & 0.88 \\
[50] & CNN and RNN & 87.11\% & 88.02\% & 86.01\% & 0.87 \\ Proposed & VGG-16-LSTM and LightGBM & 98.7\% & 98.01\% & 97.1\% & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison with state-of-the-art works along with preprocessing steps. Figure 11: Comparison of different TL algorithms in terms of Training Time. combined CNN and RNN [52] approach achieved an accuracy of 80.20% with precision, recall, and F1-score values. Notably, the proposed method, which employs VGG-16-LSTM and LightGBM, outperformed the others with a high accuracy of 92.10% and superior precision, recall, and F1-score values. However, the proposed technique with preprocessing steps achieved higher accuracy in identifying humans based on ECG spectrogram images. Additionally, Figure 12 shows different machine learning algorithms used in comparison with the LightGBM classifier. In practice, LightGBM stands out among other machine learning algorithms due to its unique combination of efficiency, scalability, and predictive power. Unlike traditional gradient boosting algorithms, LightGBM employs a novel approach to tree building, focusing on feature-based partitioning and histogram-based techniques, leading to significantly faster training times and lower memory usage. This efficiency makes LightGBM particularly well-suited for large and high-dimensional datasets, where it can handle complex feature interactions while maintaining a small computational footprint. Additionally, LightGBM's ability to handle imbalanced datasets and its built-in feature importance ranking contribute to its versatility and effectiveness in a wide range of applications. These advantages collectively position LightGBM as a compelling choice for tasks requiring accurate predictions, interpretability, and resource-efficient performance.