# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Setup
#### 3.2.2 Experiment 2  
In Experiment 2, the HR robustness was evaluated by comparing the proposed method with other five methods. During identification, all the six methods had the same denoising and fiducial point detection process, and the difference among them was the manner of heartbeat resampling and feature extraction. Six methods are shown in Table 3, where \"Y\" indicates adoption and \"N\" indicates none. In heartbeat resampling manner, \"QRS-centered\" represents the proposed novel resampling strategy, and the meaning of \"TT\" and \"TRT\" is explained below.  
\"TT\"-resampling: Segmented heartbeats with different sizes are directly resampled to 400 sample points without alignment of R points. This manner just performs a forced alignment of T wave points, ignoring all temporal period and morphological information of heartbeats.  
\"TRT\"-resampling: R and T points of segmented heartbeats are aligned, and divide each heartbeat into two parts: T-R and R-T. Then both T-R and R-T part are resampled to 200 to obtain the unified size as in Method 1. Compared with Method 1, this method further performs alignment of key fiducial points (R). It is firstly proposed by reference [20] and has proved its effectiveness on heart robustness without requiring HR measurement. All the experiments are repeated 10 times.  
Here the architecture of the used traditional CNN is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject Number**} & \multicolumn{6}{c}{**Heartbeat Number \& Estimated Heart Rate (beat/min)**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Record-1**} & \multicolumn{2}{c}{**Record-2**} & \multicolumn{2}{c}{**Record-3**} & \multicolumn{2}{c}{**Record-4**} & \multicolumn{2}{c}{**Record-5**} \\ \hline
3 & 25 & 75 & 25 & 75 & 24 & 72 & 23 & 69 & 23 & 69 \\
24  
Experiment 3  
To evaluate the effectiveness of the proposed method on short-term ECG signals, original signals in test set were segmented into hundreds of fragments with three schemes, in which a single fragment contained one, three and five heartbeats respectively. Based on these obtained short-term ECG segmentation, the subject identification accuracies were achieved and compared.  
Experiment 4  
To further validate the proposed method, the experimental results of reference [18] were compared with ours in Experiment 3. Many performance parameters such as sensitivity, specificity, precision, and F1-score were calculated and compared based on the obtained confusion matrix. For fair comparison we followed the same data distribution as reference [18], in which only Record-1 and Record-2 of each subject were employed as the training set.  
Experiment 5  
Further validation was performed by comparing the results of different methods in reference [9], namely, RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), with that of our method. Following reference [9], we evaluated the proposed method on two public databases, which were ECG-ID and MIT-BIH Arrhythmia database (MITDB). Here, the used subject number of ECG-ID database increased from 12 to 89. The MITDB is a patient information database, which contains 47 subjects and can be employed to evaluate the proposed method on the level of patient. For each subject of the MITDB, five fragments of 18-heartbeat length, which were recorded at different time, were randomly extracted for training and testing purpose. Thus a five-fold cross validation could be performed for evaluation.  
All the above experiments were made in MATLAB 2017a (MATLAB, 2017a, MathWorks, Natick, MA, USA).