1
|
Yuniarti AR, Rizal S, Lim KM. Single heartbeat ECG authentication: a 1D-CNN framework for robust and efficient human identification. Front Bioeng Biotechnol 2024; 12:1398888. [PMID: 39027407 PMCID: PMC11254790 DOI: 10.3389/fbioe.2024.1398888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
This study proposes a small one-dimensional convolutional neural network (1D-CNN) framework for individual authentication, considering the hypothesis that a single heartbeat as input is sufficient to create a robust system. A short segment between R to R of electrocardiogram (ECG) signals was chosen to generate single heartbeat samples by enforcing a rigid length thresholding procedure combined with an interpolation technique. Additionally, we explored the benefits of the synthetic minority oversampling technique (SMOTE) to tackle the imbalance in sample distribution among individuals. The proposed framework was evaluated individually and in a mixture of four public databases: MIT-BIH Normal Sinus Rhythm (NSRDB), MIT-BIH Arrhythmia (MIT-ARR), ECG-ID, and MIMIC-III which are available in the Physionet repository. The proposed framework demonstrated excellent performance, achieving a perfect score (100%) across all metrics (i.e., accuracy, precision, sensitivity, and F1-score) on individual NSRDB and MIT-ARR databases. Meanwhile, the performance remained high, reaching more than 99.6% on mixed datasets that contain larger populations and more diverse conditions. The impressive performance demonstrated in both small and large subject groups emphasizes the model's scalability and potential for widespread implementation, particularly in security contexts where timely authentication is crucial. For future research, we need to examine the incorporation of multimodal biometric systems and extend the applicability of the framework to real-time environments and larger populations.
Collapse
Affiliation(s)
- Ana Rahma Yuniarti
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, Republic of Korea
- Department of Computer Engineering, Universitas Pendidikan Indonesia, Bandung, Indonesia
| | - Syamsul Rizal
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, Republic of Korea
- School of Electronics and Electrical Engineering, Telkom University, Bandung, Indonesia
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, Republic of Korea
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, Republic of Korea
- Meta Heart Inc., Gumi-si, Republic of Korea
| |
Collapse
|
2
|
Al-Jibreen A, Al-Ahmadi S, Islam S, Artoli AM. Person identification with arrhythmic ECG signals using deep convolution neural network. Sci Rep 2024; 14:4431. [PMID: 38396036 PMCID: PMC11316829 DOI: 10.1038/s41598-024-55066-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 02/20/2024] [Indexed: 02/25/2024] Open
Abstract
Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person's beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.
Collapse
Affiliation(s)
- Awabed Al-Jibreen
- Computer Science Department, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
| | - Saad Al-Ahmadi
- Computer Science Department, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia
| | - Saiful Islam
- Department of Computer Engineering, Faculty of Engineering, TED University, 06420, Ankara, Türkiye
| | - Abdel Momin Artoli
- Computer Science Department, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia
| |
Collapse
|
3
|
Prakash AJ, Patro KK, Samantray S, Pławiak P, Hammad M. A Deep Learning Technique for Biometric Authentication Using ECG Beat Template Matching. INFORMATION 2023; 14:65. [DOI: 10.3390/info14020065] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024] Open
Abstract
An electrocardiogram (ECG) is a unique representation of a person’s identity, similar to fingerprints, and its rhythm and shape are completely different from person to person. Cloning and tampering with ECG-based biometric systems are very difficult. So, ECG signals have been used successfully in a number of biometric recognition applications where security is a top priority. The major challenges in the existing literature are (i) the noise components in the signals, (ii) the inability to automatically extract the feature set, and (iii) the performance of the system. This paper suggests a beat-based template matching deep learning (DL) technique to solve problems with traditional techniques. ECG beat denoising, R-peak detection, and segmentation are done in the pre-processing stage of this proposed methodology. These noise-free ECG beats are converted into gray-scale images and applied to the proposed deep-learning technique. A customized activation function is also developed in this work for faster convergence of the deep learning network. The proposed network can extract features automatically from the input data. The network performance is tested with a publicly available ECGID biometric database, and the proposed method is compared with the existing literature. The comparison shows that the proposed modified Siamese network authenticated biometrics have an accuracy of 99.85%, a sensitivity of 99.30%, a specificity of 99.85%, and a positive predictivity of 99.76%. The experimental results show that the proposed method works better than the state-of-the-art techniques.
Collapse
Affiliation(s)
- Allam Jaya Prakash
- Department of ECE, National Institute of Technology Rourkela, Rourkela 769008, Odisha, India
| | - Kiran Kumar Patro
- Department of ECE, Aditya Institute of Technology and Management, Tekkali 532201, Andhra Pradesh, India
| | - Saunak Samantray
- Department of ETC, IIIT Bhubaneswar, Gothapatna 751003, Odisha, India
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Menoufia P.O. Box 32511, Egypt
| |
Collapse
|
4
|
Heartprint: A Dataset of Multisession ECG Signal with Long Interval Captured from Fingers for Biometric Recognition. DATA 2022. [DOI: 10.3390/data7100141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.
Collapse
|
5
|
Jaya Prakash A, Patro KK, Hammad M, Tadeusiewicz R, Pławiak P. BAED: A secured biometric authentication system using ECG signal based on deep learning techniques. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
6
|
El Boujnouni I, Zili H, Tali A, Tali T, Laaziz Y. A wavelet-based capsule neural network for ECG biometric identification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
7
|
Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
Collapse
Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
8
|
Abstract
With the increasing demand for security and privacy, identity recognition based on the unique biometric features of ECG signals is gaining more and more attention. This paper proposes a feature reuse residual network (FRRNet) model to address the problem that the recognition accuracy of conventional ECG identification methods decreases with the increase in the number of testing samples at different moments or in different heartbeat cycles. The residual module of the proposed FRRNet model uses the adding layers of max pooling (MP) and average pooling (AP), and the proposed model splices the deep network with the shallow network to reduce noise extraction and enhance feature reuse. The FRRNet model is tested on 20 and 47 subjects under the MIT-BIH dataset, and its recognition accuracy is 99.32% and 100%, respectively. Additionally, the FRRNet model is tested on 50 and 87 subjects under the PhysioNet/Computing in Cardiology Challenge 2017 (CinC_2017) dataset, and its recognition accuracy is 94.52% and 93.51%, respectively. A total of 20 subjects are taken from the MIT-BIH and the CinC_2017 datasets for testing, and the recognition accuracy is 98.97%. The experimental results show that the FRRNet model proposed in this paper has high recognition accuracy, and the recognition accuracy is not greatly affected when the number of individuals increases.
Collapse
|
9
|
Jomaa RM, Islam MS, Mathkour H, Al-Ahmadi S. A multilayer system to boost the robustness of fingerprint authentication against presentation attacks by fusion with heart-signal. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
10
|
Ibtehaz N, Chowdhury MEH, Khandakar A, Kiranyaz S, Rahman MS, Tahir A, Qiblawey Y, Rahman T. EDITH : ECG Biometrics Aided by Deep Learning for Reliable Individual Authentication. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1109/tetci.2021.3131374] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|