# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
###### Abstract  
In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices). ECG identification; short-term ECG signals; HR-free resampling strategy; principal component analysis network; ECG-ID +
Footnote †: journal: Appliedin amplitude and duration are subtle. To deal with this problem, pattern recognition methods are preferred in ECG identification due to their reliable, quick, and objective analysis. Within the last decade, many methods, based on neural networks [5; 6], support vector machine [7], and k-nearest neighborhood [8] have been proposed in literature for the purpose of automatic identification. The literature [9] presented a recurrent neural network (RNN)-based method, which could achieve automatic feature extraction, to improve the identification performance on ECG signals from both the same session and different sessions. Discrete wavelet transform was used to extract wavelet coefficients as the feature vector and KNN was applied as the classifier in literature [10]. A novel automatic ECG identification approach combining back propagation neural network (BP-NN) with Frequency Rank Order Statistics (FROS) was introduced to distinguish different subjects in the literature [11]. The literature [12] utilized neural networks to both identify QRS complex segments and perform user authentication on these segments. All these methods mainly focus on the development of the part of feature extraction and classification. By properly combing the approaches of feature extraction with improved classifiers, these methods have achieved good performance. However, it is noted that most of them are evaluated on signals with stable heart rate (HR). Actually, there are the variation of ECG signals of one person due to HR change. For example, HR increase will shorten the duration of the ventricular depolarization period, leading to T wave shift. In other ECG applications, such as atrial fibrillation (AF) [13], the HR variability can be used to distinguish the AF episode from normal sinus rhythm. However, in ECG biometric, this variation will result in low identification task and make the identification become far more difficult without appropriate processing [14]. To overcome this limitation, a popular method is to normalize QT interval according to correction formulas. For example, [PERSON] et al. [15] scaled the ST-fragment based on [PERSON] and [PERSON]'s formulas. [PERSON] et al. [16] preferred to use a different formula based on the suggestion presented by [PERSON] in [17]. Besides methods based on QT correction, [PERSON] et al. [18] also proposed an effective feature extraction depending on the characteristic points, which were P, Q, R, S, and T. By taking less HR related parameters as features, this method was least affected by HR change.