Pith. sign in

REVIEW 3 major objections 5 minor 35 references

ECG Identity Authentication in Open-set with Multi-model Pretraining and Self-constraint Center & Irrelevant Sample Repulsion Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that open-set ECG identity authentication can be made reliable by aligning ECG signals with fiducial-feature text reports during pretraining and then constraining the feature space with center, prototype, and repulsion…

desk verdict The paper's load-bearing repulsion loss is inverted as written, and the evaluation metrics don't support the abstract's claims, but the multimodal pretraining idea is worth a second look after major fixes. read the letter →

arxiv 2504.18608 v1 pith:LUSZV4FE submitted 2025-04-25 cs.CR

Mingyu Dong , Zhidong Zhao , Hao Wang , Yefei Zhang , Yanjun Deng This is my paper · ORCID
classification cs.CR
keywords ECGidentityauthenticationopen-setrecognitionmulti-modalpretrainingcontrastivelearningreciprocalpointsself-constraintcenterbiometricsecurity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that ECG identity authentication can remain accurate and reject strangers when the test stream contains identities never seen during training. It claims that pretraining the signal encoder with contrastive alignment to text reports of fiducial features, then fine-tuning with three feature-distribution constraints, yields 99.83% authentication accuracy on the closed set and a false accept rate as low as 5.39% when open-set samples are present. Across open-set-to-closed-set ratios up to 1:10, it reports an Open-set Classification Rate above 95%. A sympathetic reader would care because real authentication systems must reject unseen impostors, and most earlier ECG methods were evaluated only with known identities.

What carries the argument

The central machinery is a two-stage training pipeline. Stage one is multi-modal contrastive pretraining: a signal encoder and a text encoder (MedCPT, a pre-trained medical text encoder) are aligned on 100,000 ECG-text pairs drawn from MIMIC-ECG, where the text is a templated report of fiducial features. Stage two is a fine-tuning loss with three terms: $L_{\text{self}}$ (the L2 distance from each sample to a class center), $L_{\text{proto}}$ (distance-based classification with learnable prototypes, replacing softmax), and $L_o$ (the irrelevant-sample repulsion term, defined as $\max(d(\mathbf{F}(x_i), \mathbf{P}_{id}) - R_{id}, 0)$ with learnable reciprocal points $\mathbf{P}$ and margin $R$). Together these losses compress each enrolled identity's features into a compact cluster while separating them from the reciprocal points that represent all other identities; the paper's intended effect is that open-set features, which were never seen, remain in the bounded region outside the registered clusters and are therefore rejected by the decision threshold.

What would settle it

Reimplement Eq. (9) exactly as printed and inspect the gradient of the loss with respect to the sample feature: minimizing $\max(d - R, 0)$ moves the sample toward the reciprocal point, so the advertised repulsion mechanism is inverted. Separately, evaluate the trained model on open-set identities drawn from a dataset not represented among the enrolled classes and check whether the OSCR and FAR remain at the reported levels.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that open-set ECG identity authentication can be made accurate without ever showing the model a true open-set sample during training. The method first aligns ECG waveforms with automatically generated text reports of five fiducial features (R-peak positions, RR intervals, QRS width, SDNN, RMSSD) using contrastive pretraining on a large signal-text dataset. During fine-tuning, three losses reshape the feature space: a self-constraint center loss pulls each identity's samples toward a class center, a dynamic-prototype distance loss replaces softmax classification with learnable prototypes, and an irrelevant-sample repulsion loss, inspired by adversarial reciprocal points, is intended to push registered samples away from the regions where other identities (serving as pseudo-open-set samples) lie. The paper reports this yields 99.83% closed-set authentication accuracy, a false accept rate as low as 5.39% when open-set samples are present, and an OSCR above 95% across open-set ratios up to 1:10.

Load-bearing premise

The method assumes that the repulsion loss as implemented actually pushes registered samples away from the reciprocal points—the printed equation would pull them closer—and that samples from other enrolled identities faithfully stand in for never-seen impostors.

Editorial extensions

If this is right

  • If the central claim holds, a deployed ECG authentication terminal can keep enrolled-user accuracy near 99.8% even when the input stream contains many people who are not registered.
  • The reported FAR of 5.39% means that in a small open-set setting, roughly 19 out of 20 unregistered users are rejected rather than mistaken for an enrolled identity.
  • Stability across open-set ratios up to 1:10 means the method does not need to know in advance how many impostor identities will appear.
  • Ablations show all three losses contribute: dropping any component raises FAR or lowers OSCR, so the gains are not attributable to pretraining alone.
  • The method transfers across three datasets with different sampling rates (500, 360, and 1000 Hz), suggesting the pipeline is not tuned to one acquisition device.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reported OSCR was measured on open-set identities drawn from the same source populations as the enrolled classes; if real-world impostors occupy feature regions the enrolled classes never covered, the true open-set rejection rate could be lower than reported.
  • The reciprocal-point repulsion idea is not specific to ECG; the same fine-tuning recipe could be tested on other physiological signals or on face and fingerprint authentication, where open-set rejection is also a practical need.
  • A falsifiable prediction follows from the method's mechanism: registered-sample features should be farther from the reciprocal points than open-set features are. That distance gap can be measured directly on any of the three datasets and compared with the claimed FAR.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes an ECG identity-authentication system for open-set scenarios. It consists of a multi-modal pretraining stage in which ECG signals are aligned with text reports generated from fiducial features, followed by fine-tuning with three loss components: self-constraint center learning, dynamic prototype learning, and irrelevant sample repulsion learning. The method is evaluated on the ECGID, MIT-BIH, and Autonomic datasets under various open-set ratios, with reported results including a closed-set accuracy of 99.83%, a False Accept Rate as low as 5.39%, and an Open-set Classification Rate above 95%. The authors claim consistent superiority over five baseline methods.

Significance. If implemented correctly, the paper would make a useful contribution to open-set ECG authentication: the evaluation uses held-out identities from three public datasets, the pretraining uses external MIMIC-ECG data, and the ablation study covers the proposed components. The headline numbers are not forced by circularity because the open-set test identities are not used for training. However, the central novelty, Irrelevant Sample Repulsion Learning, is mis-specified in the printed equations and algorithm, and no code or hyperparameters are provided, so the current manuscript does not yet substantiate the claims.

major comments (3)
  1. [Section 2.4, Eq. (9), and Algorithm 1 line 15] The loss L_o = (1/(MN)) * sum_{id,i} max(d_e(F_r(x_i), P_id) - R_id, 0) is not a repulsion term. When d_e > R_id, gradient descent decreases the loss by decreasing d_e, which moves the sample feature toward P_id; moreover, R_id is itself a learnable parameter updated to minimize the same objective, so the loss is trivially minimized by increasing R_id. The correct repulsion form would be a hinge of the opposite sign, for example max(R - d_e, 0), with R fixed or regularized. As printed, the mechanism advertised as separating registered samples from open-set samples is internally inverted or vacuous, and the experimental results, including the ablation in Table 3, cannot be interpreted as validating the paper's central claim.
  2. [Section 2.3, Eq. (4), and Eqs. (5)-(9)] Equation (4) defines C_id as an argmin over m in S_id, but the displayed objective does not reference m, so the formula does not define a center; it should presumably be C_id = argmin_{m in S_id} sum_j d(m, m_j^id). Furthermore, Equations (5), (6), (7), and (9) mix the report encoder F_r with the signal encoder F_s, for example Eq. (5) writes ||F_r(x_i^id) - C_id||_2^2 while Algorithm 1 computes distances on S_k = F_s(x_k). Because of these inconsistencies, the fine-tuning procedure as written cannot be implemented unambiguously.
  3. [Section 3.5, Table 3] The ablation results do not support the prose attribution of the FAR reduction to the irrelevant-sample repulsion module. Removing B.1 (row with A=yes, B.1=no, B.2=yes, B.3=yes) raises FAR from 7.53% to 8.40%, whereas removing B.2 or B.3 raises FAR to 15.48% and 15.11%, respectively. Combined with the issue in Eq. (9), the claim that B.1 drives the advertised open-set rejection behavior is not established by the reported experiments; the authors need to rerun the ablations with a correctly specified repulsion loss and report which component is actually responsible.
minor comments (5)
  1. [Section 2.2, Eq. (3)] Equation (3) contains the duplicate term L^{r2s}_{i,j} twice in the summand; it should presumably be L^{s2r}_{i,j} + L^{r2s}_{i,j}.
  2. [Algorithm 1] The algorithm is titled 'SimCLR's main learning algorithm', but it describes the proposed fine-tuning procedure, not SimCLR; the title should be corrected.
  3. [Equation (8)] The function zeta(.,.) used in Eq. (8) is never defined, and the connection between the inequality on P_id and the claimed separation of registered samples from open-set samples is not explained.
  4. [Abstract and Section 3.4] The headline numbers 99.83% accuracy and 5.39% FAR come from different experimental setups: the accuracy is from the Autonomic experiment with 30 enrolled identities, while the FAR comes from the ECGID experiment with 30 enrolled and 11 open-set identities. Reporting them together in the abstract is misleading and should be clarified.
  5. [General presentation] There are numerous formatting and typographical issues, including 'Hanghzou' in the affiliation, the leftover 'Preprint submitted to Physics Letters B' header, incomplete reference [19], and inconsistent notation for the batch size L versus the loss function L; these should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the reported open-set results are held-out benchmark evaluations and the pretraining uses external MIMIC-ECG data; the repulsion-loss sign anomaly is a correctness concern, not a circular reduction.

full rationale

The paper's central quantitative claims are empirical results on held-out identity classes from ECGID, MIT-BIH, and Autonomic, with multi-modal pretraining performed on the external MIMIC-ECG dataset. The fine-tuning losses (self-constraint center learning, dynamic prototype learning, and irrelevant sample repulsion learning) are training objectives defined on the training identities; they are not fitted to the test open-set identities, and the OSCR, ACC, FAR, and TNR numbers are computed on test splits that include unregistered identities, so the headline results do not reduce to the training losses by construction. The reciprocal-point mechanism uses other enrolled identities as pseudo-open-set surrogates during training, which is an approximation rather than a circular definition of the test quantity. No load-bearing self-citations or imported uniqueness theorems appear; the only external citations relevant to the method are to published baselines and standard components such as MedCPT, and none of those citations replace an independent derivation of the reported results. The printed Equation (9) and Algorithm 1 line 15 contain a hinge of the form max(d - R, 0), which, taken literally, would pull known-class features toward reciprocal points rather than repel them; however, this is an internal sign/consistency issue that undermines the claimed mechanism, not a circular equivalence between the paper's inputs and its outputs. Because no prediction is defined in terms of its target metric, no fitted parameter is renamed as a held-out result, and no self-citation carries the derivation, the paper does not exhibit circularity.

Assumptions & free parameters 6 free parameters · 6 assumptions · 1 invented entities

The method rests on standard optimization losses, on the ECG biometric assumption of identity-specific stable waveforms, and on two paper-specific assumptions: that pseudo-open-set samples from other enrolled identities represent unseen users, and that the repulsion loss in Eq. (9) has its intended sign. The latter is contradicted by the printed equation, which is why the central claim is not supported as stated.

free parameters (6)
  • contrastive temperature tau = 0.07
    Set by hand in Eqs. (1)-(2); standard CLIP-like value, not fitted to authentication data.
  • loss weights alpha, beta, gamma = not reported
    Algorithm 1 line 16 combines three losses; values never given, central trade-off between compactness and repulsion, likely tuned.
  • learnable margin R = not reported
    Introduced in Eqs. (8)-(9) to cap reciprocal point distance; learned during training, initialization and range not stated.
  • dynamic prototype vectors P_k = learned
    Learnable per-class prototype vectors in Eqs. (6)-(7); no initialization or dimension specified.
  • reciprocal point vectors P_id = learned
    Learnable per-class reciprocal points in Eqs. (8)-(9); no initialization or dimension specified.
  • open-set decision threshold delta = not reported
    FAR/TNR are reported at some threshold delta in Eqs. (10)-(12); value not given, so absolute FAR cannot be interpreted.
assumptions (6)
  • standard math Distance-based softmax in Eq. (6) provides valid classification probabilities for fine-tuning.
    Standard prototype softmax construction from [33]; used without derivation, acceptable as background.
  • domain assumption ECG signals are unique and sufficiently stable per individual to serve as biometric identity labels.
    Assumed throughout; inherited from [9], necessary for identity classification in Section 2.1 to be meaningful.
  • domain assumption Text reports built from five fiducial features (R-peak positions, RR intervals, QRS width, SDNN, RMSSD) contain identity-relevant information aligned with the raw ECG.
    Used in Section 2.2 for multimodal pretraining; no experiment isolates which fiducial features contribute and whether generated text is necessary.
  • domain assumption Pretraining on MIMIC-ECG (diagnostic ECG) transfers to identity authentication datasets.
    Section 3.1 fine-tunes on ECGID/MITBIH/Autonomic after MIMIC-ECG pretraining; no domain-gap analysis is given.
  • ad hoc to paper Pseudo-open-set samples consisting of other enrolled identities are representative of truly unseen identities.
    Section 2.4 defines irrelevant samples from non-target training classes; this surrogate is not validated against real open-set distributions.
  • ad hoc to paper Minimizing max(de(x,P_id)-R,0) in Eq. (9) repels registered samples from reciprocal points.
    The printed loss, when minimized, reduces distance to P_id until it is within margin R, i.e., it attracts rather than repels; the paper relies on the opposite sign.
invented entities (1)
  • Irrelevant sample (pseudo-open-set set)
    purpose: Surrogate for open-set data during fine-tuning to shape decision boundaries without actual unseen samples.
    Defined in Section 2.4 by treating all non-target training identities as open-set; a training construct with no external falsifiable handle, and its representativeness is assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ECG Identity Authentication in Open-set with Multi-model Pretraining and Self-constraint Center & Irrelevant Sample Repulsion Learning." pith.science (2026). https://pith.science/paper/LUSZV4FE

@misc{pith2026250418608,
  author       = {Pith},
  title        = {Pith review of: ECG Identity Authentication in Open-set with Multi-model Pretraining and Self-constraint Center & Irrelevant Sample Repulsion Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LUSZV4FE}},
  note         = {Machine review of arXiv:2504.18608}
}
read the original abstract

Electrocardiogram (ECG) signal exhibits inherent uniqueness, making it a promising biometric modality for identity authentication. As a result, ECG authentication has gained increasing attention in recent years. However, most existing methods focus primarily on improving authentication accuracy within closed-set settings, with limited research addressing the challenges posed by open-set scenarios. In real-world applications, identity authentication systems often encounter a substantial amount of unseen data, leading to potential security vulnerabilities and performance degradation. To address this issue, we propose a robust ECG identity authentication system that maintains high performance even in open-set settings. Firstly, we employ a multi-modal pretraining framework, where ECG signals are paired with textual reports derived from their corresponding fiducial features to enhance the representational capacity of the signal encoder. During fine-tuning, we introduce Self-constraint Center Learning and Irrelevant Sample Repulsion Learning to constrain the feature distribution, ensuring that the encoded representations exhibit clear decision boundaries for classification. Our method achieves 99.83% authentication accuracy and maintains a False Accept Rate as low as 5.39% in the presence of open-set samples. Furthermore, across various open-set ratios, our method demonstrates exceptional stability, maintaining an Open-set Classification Rate above 95%.

Figures

Figures reproduced from arXiv: 2504.18608 by the authors.

Figure 1
Figure 1. The proposed method is outlined in the workflow diagram, which consists of two main components: multi-modal pretraining and fine-tuning on identity [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The experimental results comparing various baseline methods are presented using ACC, OSCR, FAR, TNR, and AUC. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Line chart illustrating the variations in OSCR, FAR, and TNR as the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: T-SNE visualizations of sample feature distributions under di [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

35 extracted references · 33 canonical work pages

  1. [1]

    Arrhythmia classification using cgan-augmented ecg signals, in: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE

    Adib, E., Afghah, F., Prevost, J.J., 2022. Arrhythmia classification using cgan-augmented ecg signals, in: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 1865–1872

    work page 2022

  2. [2]

    ˙I., Choi, C., 2024

    Aslan, H. ˙I., Choi, C., 2024. Visgin: Visibility graph neural network on one-dimensional data for biometric authentication. Expert Systems with Applications 237, 121323

    work page 2024

  3. [3]

    Boumbarov, O., Velchev, Y ., Sokolov, S., 2009. Ecg personal identifi- cation in subspaces using radial basis neural networks, in: 2009 IEEE international workshop on intelligent data acquisition and advanced com- puting systems: technology and applications, IEEE. pp. 446–451

    work page 2009

  4. [4]

    Wavelet dis- tance measure for person identification using electrocardiograms

    Chan, A.D., Hamdy, M.M., Badre, A., Badee, V ., 2008. Wavelet dis- tance measure for person identification using electrocardiograms. IEEE transactions on instrumentation and measurement 57, 248–253

    work page 2008

  5. [5]

    Adversarial reciprocal points learning for open set recognition

    Chen, G., Peng, P., Wang, X., Tian, Y ., 2021. Adversarial reciprocal points learning for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 8065–8081

    work page 2021

  6. [6]

    Reducing network agnosto- phobia

    Dhamija, A.R., G ¨unther, M., Boult, T., 2018. Reducing network agnosto- phobia. Advances in Neural Information Processing Systems 31

    work page 2018

  7. [7]

    Gow, B., Pollard, T., Nathanson, L.A., Johnson, A., Moody, B., Fernan- des, C., Greenbaum, N., Waks, J.W., Eslami, P., Carbonati, T., et al.,

    work page

  8. [8]

    A survey on vision transformer

    Han, K., Wang, Y ., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y ., Xiao, A., Xu, C., Xu, Y ., et al., 2022. A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence 45, 87–110

    work page 2022

Show all 35 references
  1. [9]

    Geometrical aspects of the interindividual variability of multilead ecg recordings

    Hoekema, R., Uijen, G.J., Van Oosterom, A., 2001. Geometrical aspects of the interindividual variability of multilead ecg recordings. IEEE Trans- actions on Biomedical Engineering 48, 551–559

    2001

  2. [10]

    Hwang, S., Cha, J., Heo, J., Cho, S., Park, Y ., 2023. Multi-label ecg abnormality classification using a combined resnet-densenet architecture with resu blocks, in: 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IE...

    2023

  3. [11]

    eigenpulse: Robust human identification from cardiovascular function

    Irvine, J.M., Israel, S.A., Scruggs, W.T., Worek, W.J., 2008. eigenpulse: Robust human identification from cardiovascular function. Pattern Recog- nition 41, 3427–3435

    2008

  4. [12]

    Reading your heart: Learning ecg words and sentences via pre-training ecg language model

    Jin, J., Wang, H., Li, H., Li, J., Pan, J., Hong, S., 2025. Reading your heart: Learning ecg words and sentences via pre-training ecg language model. arXiv preprint arXiv:2502.10707

    2025 arXiv

  5. [13]

    Medcpt: Contrastive pre-trained transformers with large- scale pubmed search logs for zero-shot biomedical information retrieval

    Jin, Q., Kim, W., Chen, Q., Comeau, D.C., Yeganova, L., Wilbur, W.J., Lu, Z., 2023. Medcpt: Contrastive pre-trained transformers with large- scale pubmed search logs for zero-shot biomedical information retrieval. Bioinformatics 39, btad651

    2023

  6. [14]

    A method for stochastic optimiza- tion, in: International conference on learning representations (ICLR), San Diego, California

    Kinga, D., Adam, J.B., et al., 2015. A method for stochastic optimiza- tion, in: International conference on learning representations (ICLR), San Diego, California

    2015

  7. [15]

    Resnet 50, in: Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization

    Koonce, B., 2021. Resnet 50, in: Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization. Springer, pp. 63–72

    2021

  8. [16]

    Krishnamoorthy, L., Raju, A.S., 2024. Deep ensemble of vgg, resnet and inception for multimodal authentication system, in: 2024 Second Interna- tional Conference on Networks, Multimedia and Information Technology (NMITCON), IEEE. pp. 1–6

    2024

  9. [17]

    Efficient fiducial point detection of ecg qrs complex based on polygonal approximation

    Lee, S., Jeong, Y ., Park, D., Yun, B.J., Park, K.H., 2018. Efficient fiducial point detection of ecg qrs complex based on polygonal approximation. Sensors 18, 4502

    2018

  10. [18]

    Zero-shot ecg classification with multimodal learning and test-time clini- cal knowledge enhancement

    Liu, C., Wan, Z., Ouyang, C., Shah, A., Bai, W., Arcucci, R., 2024. Zero-shot ecg classification with multimodal learning and test-time clini- cal knowledge enhancement. arXiv preprint arXiv:2403.06659

    2024 arXiv

  11. [19]

    Biometric human identification based on electro- cardiogram

    Lugovaya, T.S., 2005. Biometric human identification based on electro- cardiogram. Master’s thesis, Faculty of Computing Technologies and In- formatics, Electrotechnical University ‘LETI’, Saint-Petersburg, Russian Federation

    2005

  12. [20]

    The impact of the mit-bih arrhythmia database

    Moody, G.B., Mark, R.G., 2001. The impact of the mit-bih arrhythmia database. IEEE engineering in medicine and biology magazine 20, 45–50

    2001

  13. [21]

    Bio- metric recognition: A systematic review on electrocardiogram data acqui- sition methods

    Pereira, T.M., Conceic ¸˜ao, R.C., Sencadas, V ., Sebasti˜ao, R., 2023. Bio- metric recognition: A systematic review on electrocardiogram data acqui- sition methods. Sensors 23, 1507

    2023

  14. [22]

    Ecg biometric recogni- tion without fiducial detection, in: 2006 Biometrics symposium: Special session on research at the biometric consortium conference, IEEE

    Plataniotis, K.N., Hatzinakos, D., Lee, J.K., 2006. Ecg biometric recogni- tion without fiducial detection, in: 2006 Biometrics symposium: Special session on research at the biometric consortium conference, IEEE. pp. 1–6

    2006

  15. [23]

    Ecg biometric analysis in differ- ent physiological recording conditions

    Por ´ee, F., Kervio, G., Carrault, G., 2016. Ecg biometric analysis in differ- ent physiological recording conditions. Signal, image and video process- ing 10, 267–276

    2016

  16. [24]

    Arrhythmia classifier based on ultra-lightweight binary neural network, in: 2023 15th International Conference on Electronics, Computers and Artificial Intel- ligence (ECAI), IEEE

    Pu, N., Wu, Z., Wang, A., Sun, H., Liu, Z., Liu, H., 2023. Arrhythmia classifier based on ultra-lightweight binary neural network, in: 2023 15th International Conference on Electronics, Computers and Artificial Intel- ligence (ECAI), IEEE. pp. 1–7

    2023

  17. [25]

    Ecgmamba: Towards ecg classification with state space models, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE

    Qiang, Y ., Dong, X., Liu, X., Yang, Y ., Hu, F., Wang, R., 2024. Ecgmamba: Towards ecg classification with state space models, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 6498–6505

    2024

  18. [26]

    Autonomic aging–a dataset to quantify changes of cardiovascular autonomic function during healthy aging

    Schumann, A., B ¨ar, K.J., 2022. Autonomic aging–a dataset to quantify changes of cardiovascular autonomic function during healthy aging. Sci- entific Data 9, 95

    2022

  19. [27]

    Deep learning applications in ecg analysis and disease detection: An investiga- tion study of recent advances

    Sumalatha, U., Prakasha, K.K., Prabhu, S., Nayak, V .C., 2024. Deep learning applications in ecg analysis and disease detection: An investiga- tion study of recent advances. IEEE Access

    2024

  20. [28]

    A comprehensive survey on ecg signals as new biometric modality for human authentication: Recent ad- vances and future challenges

    Uwaechia, A.N., Ramli, D.A., 2021. A comprehensive survey on ecg signals as new biometric modality for human authentication: Recent ad- vances and future challenges. IEEE Access 9, 97760–97802

    2021

  21. [29]

    Ecg biometric authentication using self-supervised learning for iot edge sensors

    Wang, G., Shanker, S., Nag, A., Lian, Y ., John, D., 2024. Ecg biometric authentication using self-supervised learning for iot edge sensors. IEEE Journal of Biomedical and Health Informatics

    2024

  22. [30]

    Analysis of human electrocardiogram for biometric recognition

    Wang, Y ., Agrafioti, F., Hatzinakos, D., Plataniotis, K.N., 2007. Analysis of human electrocardiogram for biometric recognition. EURASIP journal on Advances in Signal Processing 2008, 1–11

    2007

  23. [31]

    Ecg biometric recogni- tion: unlinkability, irreversibility, and security

    Wu, S.C., Hung, P.L., Swindlehurst, A.L., 2020. Ecg biometric recogni- tion: unlinkability, irreversibility, and security. IEEE Internet of Things Journal 8, 487–500

    2020

  24. [32]

    A scalable open-set ecg identification system based on compressed cnns

    Wu, S.C., Wei, S.Y ., Chang, C.S., Swindlehurst, A.L., Chiu, J.K., 2021. A scalable open-set ecg identification system based on compressed cnns. IEEE Transactions on Neural Networks and Learning Systems 34, 4966– 4980

    2021

  25. [33]

    Robust classifica- tion with convolutional prototype learning, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

    Yang, H.M., Zhang, X.Y ., Yin, F., Liu, C.L., 2018. Robust classifica- tion with convolutional prototype learning, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3474–3482

    2018

  26. [34]

    Open-world electrocardiogram classification via domain knowledge- driven contrastive learning

    Zhou, S., Huang, X., Liu, N., Zhang, W., Zhang, Y .T., Chung, F.L., 2024. Open-world electrocardiogram classification via domain knowledge- driven contrastive learning. Neural Networks 179, 106551. 10

    2024

  27. [2023]

    Type: dataset 6, 13–14

    Mimic-iv-ecg: Diagnostic electrocardiogram matched subset. Type: dataset 6, 13–14

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.