# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Year** & **Feature Extraction (Type)** & **Decision** & **ECG Dataset** & **Performance** \\ \hline [PERSON] et al. [12] & 2015 & QRS complex segments (Fiducial) & NN & ECG-ID: 90 & SI: 99.93\% \\ [PERSON] et al. [40] & 2015 & Haar Transform/GBFS & ECC-ID: 90 & SI: 83.2\% \\  & 2015 & (Non-fiducial) & KNN & MIT-BIH: 47 & SI: 95.9\% \\ [PERSON] et al. [10] & 2015 & Haar Transform and HRV/GBFS & Random Forest & ECG-ID: 90 & SI: 83.9\% \\  & (Non-fiducial) & MIT-BIH: 47 & SI: 93.1\% \\  & 21 temporal and amplitude features & & & & \\ [PERSON] et al. [41] & 2016 & and 10 morphological descriptors & SVM & MIT-BIH: 44 & SI: 98.8\% \\  & (Fiducial) & & Random & ECG-ID: 89 & SI: 100\% \\ [PERSON] et al. [42] & 2017 & Temporal, amplitude, and angle fid. & Forests + & MIT-BIH: 47 & SI: 100\% \\  & 2017 & (Non-fiducial) & RPROP & ECG-ID: 89 & SI: 96.60\% \\  & 2018 & Temporal and amplitude features & ECG-ID: 3 & SI: 98.24\% \\  & (Fiducial) & BP-NN & 10 & SI: 96.20\% \\ [PERSON] et al. [45] & 2018 & Generalized S-transformation & & 20 & SI: 94.00\% \\  & (Non-fiducial) & CNN & ECG-ID:50 & SI: 96.63\% \\ [PERSON] et al. [46] & 2018 & Mean P-QRS-T fragment + DWT & SVM & ECG-ID: 90 & SI: 99\% \\  & (Fusion of fiducial and non-fiducial) & MIT-BIH: 47 & SI: 100\% \\  & - & QRS-centered resampling strategy + & ECG-ID: 12 & SI: 97.78\% \\  & - & PCANet (partial-fiducial) & Linear-SVM & 89 & SI: 97.75\% \\  & & MIT-BIH: 47 & SI: 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison with state-of-the-art works (SI-Subject Identification). information [26; 47; 48]. According to our experimental results, the PCANets did obtain much higher heartbeat identification than the CNNs with different heartbeat resampling manner, which further proved that the PCANet method was robust to distortions. Compared with other methods in literature, our method has two main advantages. First, the method is HR robust and does not require HR based QT correction. HR variability can be removed under short-term ECG signal condition and avoid the complex operations of accurate Q detection or HR measurement. Based on the experiments, it is found that not only the amplitude, but also the temporal and morphological information of QRS can potentially contribute to identification, which is in accordance with other studies [5; 20; 21]. Second, the proposed method can achieve high heartbeat identification accuracy, which makes it suitable for systems that use a small quantity of heartbeats to make a decision [49]. Besides that, the naturality of PCANet makes our work easy to be reproduced by other researchers. Because compared with traditional neural networks (CNN), only a few parameters are required to determine a PCANet [25; 26; 48]. In the future, we will explore the \"other class\" classification problem. \"Other class\" refers to the class that classifier has not yet trained and it is totally different from the other trained individuals morphologically. In ECG identification, we can also call it unknown individual. The classification of the unknown individual is an open-set problem and can hardly be solved by some simple methods such as threshold setting [50] or distance matching [51]. So our next stage research is to improve our existing identification architecture and realize \"other class\" recognition.