# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 2 Methods
### QRS-Centered Resampling Strategy  
As mentioned above, an ECG signal can be segmented into several heartbeats. Comparison of original and processed heartbeats is shown in Figure 4. Figure 4a shows several beats extracted by taking T peaks as delimiter. It is noted that even beats \(\alpha\), \(\beta\), and \(\gamma\), which comes from the same subject, do not have the same ECG waveform pattern. This nonstandard format cannot satisfy the requirement of subsequent pattern matching, thus framing an arbitrary length of beat into a regular interval of the same length is necessary.  
Generally, resampling-process [34] can provide a sufficient way for format standardization. Heartbeats with direct resampling can be seen in Figure 4b. It is noted that QRS waveforms of beats \(\alpha\), \(\beta\), and \(\gamma\) are similar to each other, but locate in different positions on the beats. Beat \(\varphi\) has a different QRS waveform from the above three, but its position is similar to \(\alpha\). As a result, it is likely found that the similarity measurements among \(\alpha\), \(\beta\), and \(\gamma\) are larger than that between \(\alpha\) and \(\varphi\), leading the following decision to an opposite result.  
To solve this problem, a QRS-centered resampling strategy is proposed in this section. Based on the research of [5; 20; 21], our idea is that any temporal and morphological information of the QRS complex may potentially contribute to ECG identification. Meanwhile, only the amplitude is useful  
Figure 4: Comparison of heartbeats with different resampling manners. (**a**) Original heartbeats; (**b**) Heartbeats with direct resampling; (**c**) Normalization progress of the QRS-centered resampling; and (**d**) Heartbeats processed by the proposed method.  
in identifying a subject for the rest part. Thus, we remain QRS complex original to preserve all QRS information and resample the rest part to correct T wave shift by ignoring its temporal information. Figure 4c shows the proposed normalization progress. In our strategy, each heartbeat is considered as three parts, namely, the first part, the QRS part and the third part. The first part is from the start-point of beat to the start-point of the QRS part; the QRS part is centered on the detected R point and has unified 50 points; the third part is from the end-point of the QRS part to the end-point of beat. The process of the proposed strategy is summarized as follows: Firstly, considering that the QRS duration of a healthy subject is generally 60-100 ms, a 50-point width window (the digitalized frequency of ECG-ID is 500 Hz and 100 ms corresponds to 50 points on this database) centered on R point is used to determine boundaries of three parts on heartbeats; Then extracted QRS parts are aligned centered on R peaks; Lastly, resampling process is utilized to normalize the first and third part both to 175 points. Extracted beats with our strategy can be seen in Figure 4d. The morphology of beat \(\alpha\), \(\beta\), and \(\gamma\), becomes more similar to each other, and shows a significant difference from beat \(\varphi\) at the same time. The function _resample_ of MATLAB is employed as the method for resampling.