# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Results
#### 3.3.4 Experiment 4  
In this section, we compared the experimental results of [18] with ours to further validate the proposed method. Many performance parameters mentioned in [18] were calculated and compared based on the confusion matrix. The evaluation parameters include Accuracy, Sensitivity, Specificity, Precision and F1-score. The calculation process is as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[Sensitivity=\text{Recall}=\frac{TP}{TP+FN} \tag{10}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{11}\]  
\[Precision=\frac{TP}{TP+FP} \tag{12}\]  
\[F_{1}Score=\frac{2*Precision*Recall}{Precision+\text{Recall}} \tag{13}\]  
where _TP, TN, FP_, and _FN_, respectively represent True Positive, True Negative, False Positive, and False Negative. Since subject identification is a multi-class problem, this paper uses the overall statistic \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **ECG Length** & **Average Subject Identification** \\  & **(in Number of Heartbeats)** & **Accuracy (10 Experiments)** \\ \hline TT-CNN & 1 & 71.51\% \\ TT-CNN & 3 & 77.57\% \\ TT-CNN & 5 & 85.22\% \\ TT-PCANet & 1 & 77.22\% \\ TT-PCANet & 3 & 80.85\% \\ TT-PCANet & 5 & 85.82\% \\ TRT-CNN & 1 & 77.86\% \\ TRT-CNN & 3 & 83.46\% \\ TRT-CNN & 5 & 87.71\% \\ TRT-PCANet & 1 & 85.93\% \\ TRT-PCANet & 3 & 90.28\% \\ TRT-PCANet & 5 & 91.96\% \\ QRS-CNN & 1 & 83.44\% \\ QRS-CNN & 3 & 88.96\% \\ QRS-CNN & 5 & 91.89\% \\ Our Method & 1 & 89.41\% \\ Our Method & 3 & 92.49\% \\ Our Method & 5 & 94.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of subject accuracies among short-term ECG signals with different heartbeat length. of the above indicators to evaluate the performance. The general calculation form of different overall statistics is as follows:  
\[Overall\_X=\sum_{i=1}^{N}X_{i}/N_{person} \tag{14}\]  
where \(N_{person}\) is the total number of evaluated person and \(X_{i}\) represents the statistic result of the \(i\)-th person (\(X_{i}\) can be Accuracy, Recall, Specificity, Precision, or F1-score). Table 6 shows the confusion matrix for the true classification of ECG beats used for testing the proposed method. The confusion matrix shows the number of beats used for the test with their corresponding True Positive Rate (TPR). With all heartbeats voting, we can obtain the result of subject identification. Table 7 shows the confusion matrix which includes the number of signals used for testing with their corresponding subject TPR. Based on the confusion matrices of heartbeat and subject identification, the evaluation parameters can be calculated and the results of comparison can be seen in Table 8.  
In the comparison of our method with seven beats and [18], our method showed improvement on all evaluation indicators. Results showed that the overall heartbeat and subject identification accuracy could reach 88.8889% and 99.0741%, respectively. In terms of specificity, we achieved a prediction success average rate reaching 99.4949% and exceeding 95% for each person. Relative to the results of the previous method, our accuracy, precision, and F1-score were all at a high level.