Citation: | Kui-Kui Wang, Gong-Ping Yang, Lu Yang, Yu-Wen Huang, Yi-Long Yin. ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding[J]. Machine Intelligence Research, 2023, 20(5): 697-706. DOI: 10.1007/s11633-022-1345-0 |
Recent years have witnessed the surge of biometrics, such as fingerprints, faces, irises, and voices. Electrocardiogram (ECG) is also regarded as an inherent liveness biometric trait[1] with the advantage of being captured from a living individual. Most pioneering methods are designed to extract discriminative features from ECG signals based on fiducial or non-fiducial. For example, 19 stable fiducial features[2] related to interval, amplitude, and angle were computed from each heartbeat. The non-fiducial methods usually used wavelet features[3], statistical features, or autocorrelation (AC) features[4, 5]. Besides, several works with sparse representation[6–8] and dimensionality reduction, e.g., principal component analysis (PCA), kernel PCA (KPCA), linear discriminant analysis (LDA)[9–11], have also shown the success in ECG biometrics.
Although previous research works have gained promising performance, one limitation is that the existing methods cannot comprehensively exploit the diverse information among the ECG. During the collection, ECG signals are easily affected by changes in physiological and psychological activity, such as emotional factors, diets, diseases, electrode position, and other factors[12], causing the diversities between heartbeats of homologous samples to be large while heterologous samples are smaller. Hence, it is necessary to alleviate the impact of diversity and learn a discriminative feature representation to improve the recognition performance.
Motivated by the success of matrix factorization in other tasks, several promising methods[13, 14] based on matrix factorization for ECG biometrics have been proposed. Wang et al.[15] proposed a multi-scale differential feature with collective matrix factorization to generate a robust ECG representation. Li et al.[16] opted graph regularized non-negative matrix factorization and sparse representation to obtain non-fiducial features. Huang et al.[17] designed a multi-feature collective non-negative matrix factorization model. However, most matrix factorization methods for ECG biometrics aim to learn the latent discriminative representation without fully exploring the correlation between the base feature and latent representation to alleviate the impact of sample diversity.
In this paper, we focus on exploring the ECG sample diversity by taking advantage of matrix factorization to learn a latent representation. There are two problems to be considered. The first is how to minimize the sample diversity to improve recognition performance and the second is how to construct a strong correlation between the base feature space and the latent space to enhance the discriminative capability of learned representation.
To handle the above issues, we propose a novel framework, termed enhanced correlation and semantic-rich embedding matrix factorization for ECG biometrics. In specific, to acquire similar representations for homologous samples of the same subject and push away the representations of heterologous samples from different subjects, we introduce the pairwise similarity among all samples into our framework. For deep mining and accurately embedding of the intrinsic information of the base feature, we construct an enhanced correlation between the base feature and the latent representation. To further enhance the discriminative capability of the learned representation, we make orthogonal constraints on it. Finally, a novel optimization algorithm is proposed to learn our model. The main contributions of this paper are summarized as follows:
1) A new framework is proposed to effectively learn the latent representation of ECG signals. In this framework, pairwise similarity, label vectors, and base features work seamlessly and collectively to ensure high-quality latent representation.
2) To optimize the proposed novel objective function, an effective learning algorithm is presented. Through this algorithm, we could accurately and quickly solve the loss function.
3) We perform extensive experiments on two benchmark datasets and the experimental results demonstrate the effectiveness of our method.
The remainder of this paper is organized as follows. Related work is presented in Section 2. The details of our proposed method and matching procedure are presented in Section 3. Section 4 shows the experimental results and the conclusions are presented in Section 5.
ECG is a physiological signal generated from the contraction and the recovery of the heart. Generally, there are two main types of ECG acquisition settings for ECG biometrics, i.e., on-the-person and off-the-person. On-the-person acquisition usually uses multiple electrodes attached to the skin surface, such as medical datasets MIT-BIH arrhythmia database (MIT-BIH)[18]. For off-the-person datasets, they usually acquire ECG signals using dry button electrodes held by the subjects in contact with their finger, wrist, and so on, such as check your biosignals here initiative (CYBHiDB)[19]. Specifically, off-the-person acquisition addresses the reduced acceptability and comfort, and it has lower signal-to-noise ratios, which means considerably more noise influence.
The ECG signal is a cyclic repetition of five fiducial points, i.e., P, Q, R, S, and T waves. And the existing ECG biometric recognition methods can be roughly divided into three categories, i.e., fiducial methods, non-fiducial methods, and deep learning-based methods. For the fiducial methods, features are extracted employing the fiducial points of heartbeat, such as amplitudes, time duration, QRS complex, angles, slopes, areas, etc. Some methods[20–25] extract a class of features from dominant fiducial points of the ECG waveform. Non-fiducial methods do not use the fiducial points to generate the feature set, but focus on the whole signal or segmented by a sliding window. Some previous works have shown the success using the Gaussian model[26], statistical features[27], one-dimensional multi-resolution local binary patterns (1DMRLBP)[28], discrete wavelet transformation (DWT), and kernel methods[29] to extract non-fiducial features.
With the popularity of deep neural networks, some pioneer works[30, 31] adopting DNN for effective feature representations, have been proposed. Salloum and Kuo[32] adopted the LSTM-based RNN for ECG biometrics without any feature extraction. Zhao et al.[33] integrated a generalized S-transformation and CNN for human identification. Labati et al.[34] extracted significant features from one or more leads using a deep CNN. Especially, deep learning-based models can directly deal with ECG signals with blind segmentation rather than handcrafted features, and they can perform significantly better than most non-deep methods. However, most of them are time-consuming and lack interpretability. Thus, we mainly focus on how to efficiently refine the reliable base feature for ECG biometrics in our paper.
Assuming that the training set is formed by
Given the base feature matrix
|
(1) |
where
Meanwhile, we could learn a projection matrix
|
(2) |
By combining the above two equations, we could extract more information from
|
(3) |
where
The diversity of ECG signals is a primary factor affecting the recognition performance, and how to capture more semantic information is another essential problem.
Undoubtedly, homologous samples of the same subject should have as similar latent representations as possible, while the representations of heterologous samples from different subjects should be pushed away. To fulfill this purpose, we first define the pairwise similarity among all samples from label information
|
(4) |
which has a symmetric form with respect to
Then, we intend to move a step further towards preserving semantic-rich information. Similarly, the matrix factorization technique helps
|
(5) |
where
As both (4) and (5) try to preserve the semantic information of samples, we try to reformulate them into one equation:
|
(6) |
where
By combining (3) and (6), the overall objective function can be given as follows:
|
(7) |
where
To solve the optimization problem in (7), we propose an alternating optimization algorithm by the following steps.
Step 1. Update
|
(8) |
We set the gradient of the objective function with respect to
|
(9) |
Step 2. Update
|
(10) |
Analogous to P, we set the derivative of the objective with respect to
|
(11) |
Step 3. Update
|
(12) |
To solve the above problem, we first transform (12) into a matrix trace form with the constrains of
|
(13) |
We define
|
(14) |
where
|
(15) |
By the above steps, we can update all the variables and repeat the process iteratively until the objective function converges. Finally, to show the overall view of the optimization, we summarize it in Algorithm 1.
Algorithm 1. Optimization algorithm
Input: Base feature matrix of training data
Output: Projection matrix
Procedure:
Randomly initialize P,
for
Update
Update
Update
end for
Return: P.
To further comprehend our method, we give a theoretical convergence analysis for our model. Denote
After the training procedure, the projection matrix P can be learned, which is used to reconstruct the discriminative representations for query samples and enroll samples, represented by PXquery and PXenroll, respectively. In the matching stage, for each heartbeat vector of the query samples,
|
(16) |
CYBHiDB[19] is acquired from the palms and fingertips, which is regarded as a challenging off-the-person dataset. In this paper, we used the signals in the long-term, and it contains 63 healthy participants, collected from two sessions with three-month intervals, and we called them T1 and T2, respectively. MIT-BIH[18] is one of the most widely used datasets for ECG biometrics, and it is available in the Physionet[36] repository. It contains 48 two-channel ECG recordings from 47 subjects. Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database (PTB)[37] includes 549 recordings from 290 subjects collected using conventional 12 leads together with the 3 Frank leads. In this paper, we choose 248 subjects whose range is longer than 100 s, and each subject has one recording.
In the heartbeat segmentation stage, we detect the R peak with Pan-Tompkins[38], taking 299 sampling points forward from the R peak and 300 sampling points backward, and a total of 600 sampling points as a heartbeat on CYBHiDB. For MIT-BIH, we take 100 sampling points before the location of R and 159 sampling points after the location of R, with 260 sampling points in one heartbeat. For PTB, there are 460 sampling points in one heartbeat. Then we extracted 1DMRLBP as a base feature, which served as the input of our framework. Each individual has nine homologous samples in the training set and five homologous samples in the testing set. The dimension of the latent representation
We employed the widely used criteria equal error rate (EER) to evaluate the performance in verification mode and EER is the point where false rejection rate (FRR) is equal to the false acceptance rate (FAR). For the identification mode, the accuracy is used as the evaluation criteria, which is the percentage of correctly identified testing samples.
We compared the performance of our method with several state-of-the-art methods on MIT-BIH, including non-deep methods[4, 15, 39] and deep learning-based methods[32, 40, 41]. For all baselines, the results are copied from their original papers. The experimental results are summarized in Table 1, and we have the following observations: 1) Our method outperforms all non-deep baselines in MIT-BIH, demonstrating its effectiveness for ECG biometrics. One reason is that our model can better extract semantic information because of the elaborate design of semantic-rich information preserving. The other reason is that our model constructs an enhanced correlation between the base feature and latent representation, reinforcing the discriminative capability of learned representation. Therefore, compared with other traditional non-deep methods, it is able to generate a more discriminative representation. 2) Compared to deep-learning methods, our model achieves comparable performance. The deep model[41] uses 1D-CNN together with attention Bi-LSTM for identification, where it achieves satisfying performance at the cost of much more training time. Especially, our method is superior to these two deep baselines[32, 40] on both accuracy and EER. Therefore, our method can achieve competitive or even superior performance on ECG biometric recognition.
We also conducted the experiments on the challenging off-the-person dataset CYBHiDB in two situations, i.e., within-session and across-session. The experiments in within-session situation use training data and testing data in the same session. For the across-session situation, training data and testing data come from different sessions, where the training data come from the T1 session, while testing data come from the T2 session or training data are from T2 and testing data are from the T1. The baselines for CYBHiDB include non-deep methods[3, 7, 42, 43] and deep method[44]. The experimental results are summarized in Tables 2 and 3. For all baselines, the results are those reported in previous work[7]. From Table 2, we can observe that our model achieves the best accuracy in the within-session for identification mode. For the verification mode, our model achieves competitive EER results. The baseline[7] obtains a better EER because it fused three features (shape, 1dlbp, and wavelet). The deep learning method[44] gets satisfying EER in verification mode, while the accuracy in identification mode is insignificant and time-consuming. For the across-session situation, the experimental results are summarized in Table 3. It is worth noting that our model achieves satisfying performance on both accuracy and EER. The main reason is that our model can preserve semantic information well and enhance the correlation between the base feature and latent representation. In this respect, it further corroborates the effectiveness of our approach.
Method | Training | Testing | EER (%) | Accuracy (%) |
Chan et al.[3] | T1 | T2 | 20.47 | 76.11 |
T2 | T1 | 18.29 | 79.38 | |
Huang et al.[7] | T1 | T2 | 10.26 | 87.75 |
T2 | T1 | 11.14 | 86.24 | |
Islam and Alajlan[42] | T1 | T2 | 15.23 | 82.49 |
T2 | T1 | 14.78 | 83.83 | |
Odinaka et al.[43] | T1 | T2 | 14.04 | 83.23 |
T2 | T1 | 13.18 | 84.35 | |
Da Silva Luz et al.[44] | T1 | T2 | 12.78 | 85.46 |
T2 | T1 | 12.83 | 84.46 | |
Ours | T1 | T2 | 6.17 | 92.86 |
T2 | T1 | 5.86 | 96.03 |
Besides, we conducted experiments on PTB, and the baselines include deep learning-based methods[33, 41] and non-deep methods[7, 45, 46]. Experimental results are summarized in Table 4. For all baselines, the results are those reported in previous work[45]. From Table 4, we can observe that our method outperforms all non-deep baselines and achieves comparable accuracy compared with deep-learning methods. In other words, our method outperforms (or is in part with) existing methods. Taking into account both performance and efficiency, our proposed method may be the most practical.
In this section, we conducted experiments to analyze the sensitivity of parameters including
In a nutshell, although there are several parameters in our framework, most of them are not sensitive and could be easily tuned in practice.
In this section, we summarize the feature extraction time and matching time on MIT-BIH to verify the efficiency of our method. The results are reported in Table 5, and we compute the average feature extraction time per sample and the average matching time per sample pair repeated 20 times. Table 5 shows that our method consumes less time no matter the process of feature extraction or matching procedure, and we can conclude that our method is efficient.
In this section, we conducted ablation experiments on MIT-BIH to investigate the effectiveness of enhanced correlation and semantic-rich embedding, separately. In particular, we design a variant of our method called OURS_1 by setting
Method | Accuracy (%) |
OURS_1 | 95.74 |
OURS_2 | 96.28 |
Ours | 99.1 |
In Section 3.6, we proved that the iterative optimization is convergent, and we further conducted experiments to investigate the convergence on two datasets. Specifically, we recorded the accuracy as the iteration increased, and the convergence curves are plotted in Fig. 2. As shown in Fig. 2, the optimization algorithm converges very fast, and the performance increases and tends to be stable after several iterations within five iterations on two datasets, thereby distinctly saving the time required for training.
In this paper, we propose a new framework for ECG biometrics based on constructing enhanced correlation and embedding semantic-rich information. Firstly, the correlation between the base feature and latent representation can be constructed, and we further enhance the correlation by formulating the bidirectional projections with one matrix. Secondly, we design an asymmetric loss function, which includes both the label matrix and the pairwise similarity among the samples, to embed the semantic-rich information into the learning of latent representation for the ECG signal. Then, we combine both enhanced correlation and semantic-rich embedding together to derive the final objective loss and propose an effective algorithm to optimize it. Extensive experiments on two widely-used datasets demonstrate that the proposed framework outperforms the state-of-the-art. In future work, we will explore online ECG biometrics to handle incremental datasets with new instances.
The authors declared that they have no conflicts of interest to this work.
This work was supported by National Natural Science Foundation of China (No. 62076151), Natural Science Foundation of Shandong Province, China (No. ZR2020MF052), and the NSFC-Xinjiang Joint Fund, China (No. U1903127).
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1. | Xiang-Ru Yu, Deng-Bao Wang, Min-Ling Zhang. Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive Approach. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(8): 3765. DOI:10.1109/TKDE.2024.3367721 |
Method | Training | Testing | EER (%) | Accuracy (%) |
Chan et al.[3] | T1 | T2 | 20.47 | 76.11 |
T2 | T1 | 18.29 | 79.38 | |
Huang et al.[7] | T1 | T2 | 10.26 | 87.75 |
T2 | T1 | 11.14 | 86.24 | |
Islam and Alajlan[42] | T1 | T2 | 15.23 | 82.49 |
T2 | T1 | 14.78 | 83.83 | |
Odinaka et al.[43] | T1 | T2 | 14.04 | 83.23 |
T2 | T1 | 13.18 | 84.35 | |
Da Silva Luz et al.[44] | T1 | T2 | 12.78 | 85.46 |
T2 | T1 | 12.83 | 84.46 | |
Ours | T1 | T2 | 6.17 | 92.86 |
T2 | T1 | 5.86 | 96.03 |
Method | Accuracy (%) |
OURS_1 | 95.74 |
OURS_2 | 96.28 |
Ours | 99.1 |