# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Results
#### 3.3.5 Experiment 5  
The goal of this experiment is to compare the results of our method with that of reference [9], which is one of the state-of-the-art methods for ECG identification. Table 9 shows the comparison between reference [9] and the proposed method.  
Results on Table 9 show that LSTM network performs better than GRU and traditional RNN in terms of the subject identification accuracy, and that our approach behaves similar to the LSTM network. On the databases, ECG-ID, and the MITDB, subject identification accuracies of 97.75% and 100% were achieved. Besides that, the proposed method could achieve high heartbeat identification accuracy of 84.5% and 90.45% on ECG-ID database and the MITDB, which made it possible to obtain good performance based on short-term signals. For ECG-ID and the MITDB, 95.25% and 97.85% subject identification accuracy were obtained even with signals of only seven-heartbeat length. Especially, the subject identification accuracy of signals of three-heartbeat length could reach 96.96% on the MITDB.  
These results further proved the effectiveness of our method for short-term ECG signal identification.  
Table 10 shows the state-of-the-art methods proposed for ECG biometric identification. Compared with other methods, the proposed method achieves high-level results in terms of subject identification accuracy on both databases, which suggests that it can serve as an effectively partial-fiducial way for ECG biometric identification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No** & **Performance Parameter** & **Value [18]** & **Value (Our Method)** \\ \hline
1 & True\_Positive Rate (Beat Identification) & 80.5556\% & 88.8889\% \\
2 & True\_Positive Rate (Subject Identification) & 88.8889\% & 94.4444\% \\
3 & Overall\_Accuracy & 98.1481\% & 99.0741\% \\
4 & Overall\_Recall & 88.8889\% & 94.4444\% \\
5 & Overall\_Specificity & 98.9899\% & 99.4949\% \\
6 & Overall\_Precision & 93.750\% & 96.6667\% \\
7 & Overall\_F1 Score & 88.5317\% & 93.7500\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of our method with seven beats and literature [18].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**ECG-ID: 89**} & \multicolumn{2}{c}{**MIT-BIH: 47**} \\ \cline{2-5}
**Methods** & \begin{tabular}{c} **ECG Length** \\ **(in Number of Heartbeats)** \\ \end{tabular} & \begin{tabular}{c} **SI** \\ **SI** \\ \end{tabular} & \
\begin{tabular}{c} **ECG Length** \\ **(in Number of Heartbeats)** \\ \end{tabular} &
\begin{tabular}{c} **SI** \\ **SI** \\ \end{tabular} \\ \hline RNN [9] & 18 & 91.7\% & 18 & 93.3\% \\ GRU [9] & 18 & 94.4\% & 18 & 95.7\% \\ LSTM [9] & 18 & 100\% & 18 & 100\% \\ Proposed method & 18 & 97.75\% & 18 & 100\% \\ Proposed method & 7 & 95.25\% & 7 & 97.85\% \\ Proposed method & 5 & 92.36\% & 5 & 97.80\% \\ Proposed method & 3 & 89.97\% & 3 & 96.96\% \\ Proposed method & 1 & 84.50\% & 1 & 90.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the proposed method with reference [9] (SI-Subject Identification, HI-Heartbeat Identification).