Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

Abstract

This paper presents a Multilayer Perception Neural Network developed to identify human subjects using electrocardiogram (ECG) signals. We use the amplitude values of Q, R and S as a features for our experiments. In this study, a total of 87 dataset were collected among 14 subjects from the Physikalisch-Technische Bundesanstalt (PTB) database. Out of the 14 subjects, Q-R-S feature points were taken from different day and time sessions to perform classification with MLP. Out of this data, 66 % is used as training dataset while the remaining 34 % is used for testing. Our method yields 96 % accuracy and demonstrates that the use of three fiducial points is sufficient to identify a subject despite the common practice of taking more feature points.

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

“…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].…”

“…To preserve all potential identity information of the QRS complex, we use a length-fixed window to keep the QRS complex original. Secondly, it is also found that mapping the heartbeat into a regular interval of segments does help to deal with the HR variability problem [5,20]. Thus, we segment the heartbeat into three parts, which are the first, the QRS and the third part respectively, and unify them.…”

“…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 in identifying a subject for the rest part.…”

“…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.…”

A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding

“…In prior literature, Nemirko et al [5] firstly pointed out that QRS complex did not change significantly with the heart rate varying, and was able to perform as a stable characteristic. Later, Tuerxunwaili et al [31] found that using only three QRS based features was sufficient to identify a subject, which further highlighted the importance of QRS complex. Actually, to our knowledge, nearly all the existing ECG based biometric identification methods have taken QRS complex or its related form as features.…”