Статті в журналах: "Ecg biometric"

1

Kumar, Sunil. "ECG Biometric Identification." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 2148–52. http://dx.doi.org/10.22214/ijraset.2018.3505.

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TALININGSIH, FAUZI FRAHMA, YUNENDAH NUR FU’ADAH, SYAMSUL RIZAL, ACHMAD RIZAL, and MUHAMMAD ADNAN PRAMUDITO. "Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi." MIND Journal 7, no. 1 (June 29, 2022): 1–10. http://dx.doi.org/10.26760/mindjournal.v7i1.1-10.

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ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Network

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3

Ammour, Nassim, Rami M. Jomaa, Md Saiful Islam, Yakoub Bazi, Haikel Alhichri, and Naif Alajlan. "Deep Contrastive Learning-Based Model for ECG Biometrics." Applied Sciences 13, no. 5 (February 27, 2023): 3070. http://dx.doi.org/10.3390/app13053070.

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The electrocardiogram (ECG) signal is shown to be promising as a biometric. To this end, it has been demonstrated that the analysis of ECG signals can be considered as a good solution for increasing the biometric security levels. This can be mainly due to its inherent robustness against presentation attacks. In this work, we present a deep contrastive learning-based system for ECG biometric identification. The proposed system consists of three blocks: a feature extraction backbone based on short time Fourier transform (STFT), a contrastive learning network, and a classification network. We evaluated the proposed system on the Heartprint dataset, a new ECG biometrics multi-session dataset. The experimental analysis shows promising capabilities of the proposed method. In particular, it yields an average top1 accuracy of 98.02% on a new dataset built by gathering 1539 ECG records from 199 subjects collected in multiple sessions with an average interval between sessions of 47 days.

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4

Elshahed, Marwa A. "Personal identity verification based ECG biometric using non-fiducial features." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 3007. http://dx.doi.org/10.11591/ijece.v10i3.pp3007-3013.

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Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

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Singh, Yogendra Narain, Sanjay Kumar Singh, and Amit Kumar Ray. "Bioelectrical Signals as Emerging Biometrics: Issues and Challenges." ISRN Signal Processing 2012 (July 26, 2012): 1–13. http://dx.doi.org/10.5402/2012/712032.

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This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

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6

Kim, Hanvit, Haena Kim, Se Chun, Jae-Hwan Kang, Ian Oakley, Youryang Lee, Jun Ryu, et al. "A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors." Sensors 18, no. 8 (August 20, 2018): 2738. http://dx.doi.org/10.3390/s18082738.

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Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD . 1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD . 1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

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7

Chou, Ching-Yao, Yo-Woei Pua, Ting-Wei Sun, and An-Yeu (Andy) Wu. "Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis." Sensors 20, no. 11 (June 9, 2020): 3279. http://dx.doi.org/10.3390/s20113279.

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Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

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Karimian, Nima, Damon Woodard, and Domenic Forte. "ECG Biometric: Spoofing and Countermeasures." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 3 (July 2020): 257–70. http://dx.doi.org/10.1109/tbiom.2020.2992274.

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9

S. Raju, A., and V. Udayashankara. "A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 689. http://dx.doi.org/10.14419/ijet.v7i4.36.24224.

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Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner for those researchers, who wish to persue their work in the area of biometrics.

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Donida Labati, Ruggero, Enrique Muñoz, Vincenzo Piuri, Roberto Sassi, and Fabio Scotti. "Deep-ECG: Convolutional Neural Networks for ECG biometric recognition." Pattern Recognition Letters 126 (September 2019): 78–85. http://dx.doi.org/10.1016/j.patrec.2018.03.028.

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11

Tirado-Martin, Paloma, and Raul Sanchez-Reillo. "BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets." Applied Sciences 11, no. 13 (June 24, 2021): 5880. http://dx.doi.org/10.3390/app11135880.

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Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

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12

Wang, Min, Xuefei Yin, Yanming Zhu, and Jiankun Hu. "Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey." Sensors 22, no. 14 (July 7, 2022): 5111. http://dx.doi.org/10.3390/s22145111.

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Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.

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13

Yoon, Seok-Joo, and Gwang-Jun Kim. "Personal Biometric Identification based on ECG Features." Journal of the Korea institute of electronic communication sciences 10, no. 4 (April 30, 2015): 521–26. http://dx.doi.org/10.13067/jkiecs.2015.10.4.521.

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14

Pereira, Teresa M. C., Raquel C. Conceição, Vitor Sencadas, and Raquel Sebastião. "Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods." Sensors 23, no. 3 (January 29, 2023): 1507. http://dx.doi.org/10.3390/s23031507.

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In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.

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Yadav, Nisha, Manoj Duhan, and Anil Rose. "Biometric Human Recognition using ECG Signals." IARJSET 4, no. 6 (June 20, 2017): 168–71. http://dx.doi.org/10.17148/iarjset.2017.4630.

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16

Odinaka, Ikenna, Po-Hsiang Lai, Alan D. Kaplan, Joseph A. O'Sullivan, Erik J. Sirevaag, and John W. Rohrbaugh. "ECG Biometric Recognition: A Comparative Analysis." IEEE Transactions on Information Forensics and Security 7, no. 6 (December 2012): 1812–24. http://dx.doi.org/10.1109/tifs.2012.2215324.

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17

Kadhim Abed, Jameel, and Nahrain N. Abd. "ECG Biometric Verification by using PCA." International Journal of Engineering Trends and Technology 65, no. 1 (November 25, 2018): 4–8. http://dx.doi.org/10.14445/22315381/ijett-v65p202.

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18

Ingale, Mohit, Renato Cordeiro, Siddartha Thentu, Younghee Park, and Nima Karimian. "ECG Biometric Authentication: A Comparative Analysis." IEEE Access 8 (2020): 117853–66. http://dx.doi.org/10.1109/access.2020.3004464.

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19

Prakash, Allam Jaya, Kiran Kumar Patro, Saunak Samantray, Paweł Pławiak, and Mohamed Hammad. "A Deep Learning Technique for Biometric Authentication Using ECG Beat Template Matching." Information 14, no. 2 (January 23, 2023): 65. http://dx.doi.org/10.3390/info14020065.

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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.

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20

Noh, Seungil, Jaehan Kim, Seokmin Lee, Youngshin Kang, Cheolsoo Park, and Youngjoo Shin. "Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes." Security and Communication Networks 2022 (September 29, 2022): 1–14. http://dx.doi.org/10.1155/2022/7997509.

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Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.

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21

M. Jomaa, Rami, Hassan Mathkour, Yakoub Bazi, and Md Saiful Islam. "End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection." Sensors 20, no. 7 (April 7, 2020): 2085. http://dx.doi.org/10.3390/s20072085.

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Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

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22

Agrafioti, Foteini, Francis M. Bui, and Dimitrios Hatzinakos. "Secure Telemedicine: Biometrics for Remote and Continuous Patient Verification." Journal of Computer Networks and Communications 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/924791.

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The technological advancements in the field of remote sensing have resulted in substantial growth of the telemedicine industry. While health care practitioners may now monitor their patients’ well-being from a distance and deliver their services remotely, the lack of physical presence introduces security risks, primarily with regard to the identity of the involved parties. The sensing apparatus, that a patient may employ at home, collects and transmits vital signals to medical centres which respond with treatment decisions despite the lack of solid authentication of the transmitter’s identity. In essence, remote monitoring increases the risks of identity fraud in health care. This paper proposes a biometric identification solution suitable for continuous monitoring environments. The system uses the electrocardiogram (ECG) signal in order to extract unique characteristics which allow to discriminate users. In security, ECG falls under the category ofmedical biometrics, a relatively young but promising field of biometric security solutions. In this work, the authors investigate the idiosyncratic properties of home telemonitoring that may affect the ECG signal and compromise security. The effects of psychological changes on the ECG waveform are taken into consideration for the design of a robust biometric system that can identify users based on cardiac signals despite physical or emotional variations.

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23

Sun, Ting-Wei, Danish Ali, and Ayeu (Andy) Wu. "Compressed-Domain ECG-based Biometric User Identification Using Task-Driven Dictionary Learning." ACM Transactions on Computing for Healthcare 3, no. 3 (July 31, 2022): 1–15. http://dx.doi.org/10.1145/3461701.

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In recent years, user identification has become crucial for authorized machine access. Electrocardiography (ECG) is a new and rising biometrics signature. Rather than traditional biological traits, ECG cannot be easily imitated. In the long-term monitoring system, the wireless wearable ECG biomedical sensor nodes are resource-limited. Recently, compressive sensing (CS) technology is extensively applied to reduce the power of data transmission and acquisition. The prior CS-based reconstruction process aims at improving energy efficiency with different schemes, and they focus on the performance of reconstruction only. Therefore, we present a sparse coding-based classifier, trained by task-driven dictionary learning (TDDL), to realize low-complexity user identification in compressed-domain directly. TDDL is one of the dictionary learning and designed for classification tasks. It co-optimizes the dictionary and classifier weighting simultaneously, which gives better accuracy. In this article, we are proposing a TDDL-based compression learning algorithm for ECG biometric user identification as this directly identifies user identity (ID) without undergoing reconstruction process and conventional classifier. It can extract necessary information from the compressed-ECG signal directly to save the system power and computational complexity. The algorithm has 2%–10% accuracy improvements compared with state-of-the-art algorithms and maintains low complexity at the same time. Our proposed TDDL-CL will be the better choice in the long-term wearable ECG biometric devices.

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Ibrahim, Anwar E., Salah Abdel-Mageid, Nadra Nada, and Marwa A. Elshahed. "Human Identification Using Electrocardiogram Signal as a Biometric Trait." International Journal of System Dynamics Applications 11, no. 3 (August 2, 2022): 1–17. http://dx.doi.org/10.4018/ijsda.287113.

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Biometrics is an interesting study due to the incredible progress in security. Electrocardiogram (ECG) signal analysis is an active research area for diagnoses. Various techniques have been proposed in human identification system based on ECG. This work investigates in ECG as a biometric trait which based on uniqueness represented by physiological and geometrical of ECG signal of person.In this paper, a proposed non-fiducial identification system is presented with comparative study using Radial Basis Functions (RBF) neural network, Back Propagation (BP) neural network and Support Vector Machine (SVM) as classification methods. The Discrete Wavelet Transform method is applied to extract features from the ECG signal. The experimental results show that the proposed scheme achieves high identification rate compared to the existing techniques. Furthermore, the two classifiers RBF and BP are integrated to achieve higher rate of human identification.

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BE, Manjunathswamy, Appaji M Abhishek, Thriveni J, Venugopal K R, and L. M Patnaik. "Multimodal Biometric Authentication using ECG and Fingerprint." International Journal of Computer Applications 111, no. 13 (February 18, 2015): 33–39. http://dx.doi.org/10.5120/19601-1452.

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Astapov, A. A., D. V. Davydov, A. I. Egorov, D. V. Drozdov, and E. M. Glukhovskij. "ECG-based biometric identification: some modern approaches." Bulletin of Russian State Medical University, no. 1 (2016): 35–39. http://dx.doi.org/10.24075/brsmu.2016-01-06.

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27

Srivastva, Ranjeet, Yogendra Narain Singh, and Ashutosh Singh. "Statistical independence of ECG for biometric authentication." Pattern Recognition 127 (July 2022): 108640. http://dx.doi.org/10.1016/j.patcog.2022.108640.

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WU, Hung-Tsai, Yi-Ting WU, and Wen-Whei CHANG. "Biometric Identification Using JPEG2000 Compressed ECG Signals." IEICE Transactions on Information and Systems E98.D, no. 10 (2015): 1829–37. http://dx.doi.org/10.1587/transinf.2015edp7035.

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Agrafioti, Foteini, and Dimitrios Hatzinakos. "ECG biometric analysis in cardiac irregularity conditions." Signal, Image and Video Processing 3, no. 4 (September 2, 2008): 329–43. http://dx.doi.org/10.1007/s11760-008-0073-4.

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El_Rahman, Sahar A. "Biometric human recognition system based on ECG." Multimedia Tools and Applications 78, no. 13 (January 8, 2019): 17555–72. http://dx.doi.org/10.1007/s11042-019-7152-0.

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Wu, Shun-Chi, Pei-Lun Hung, and A. Lee Swindlehurst. "ECG Biometric Recognition: Unlinkability, Irreversibility, and Security." IEEE Internet of Things Journal 8, no. 1 (January 1, 2021): 487–500. http://dx.doi.org/10.1109/jiot.2020.3004362.

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Rezgui, Dhouha, and Zied Lachiri. "ECG biometric recognition using SVM-based approach." IEEJ Transactions on Electrical and Electronic Engineering 11 (June 2016): S94—S100. http://dx.doi.org/10.1002/tee.22241.

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Hammad, Mohamed, Gongning Luo, and Kuanquan Wang. "Cancelable biometric authentication system based on ECG." Multimedia Tools and Applications 78, no. 2 (July 3, 2018): 1857–87. http://dx.doi.org/10.1007/s11042-018-6300-2.

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34

Shdefat, Ahmed Younes, Moon-Il Joo, Sung-Hoon Choi, and Hee-Cheol Kim. "Utilizing ECG Waveform Features as New Biometric Authentication Method." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 658. http://dx.doi.org/10.11591/ijece.v8i2.pp658-665.

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Анотація:

<p>In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.</p>

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35

AlDuwaile, Dalal A., and Md Saiful Islam. "Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition." Entropy 23, no. 6 (June 9, 2021): 733. http://dx.doi.org/10.3390/e23060733.

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The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

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36

Lourenço, André, Hugo Silva, and Ana Fred. "Unveiling the Biometric Potential of Finger-Based ECG Signals." Computational Intelligence and Neuroscience 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/720971.

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The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

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37

Zhou, Ruishi, Chenshuo Wang, Pengfei Zhang, Xianxiang Chen, Lidong Du, Peng Wang, Zhan Zhao, Mingyan Du, and Zhen Fang. "ECG-based biometric under different psychological stress states." Computer Methods and Programs in Biomedicine 202 (April 2021): 106005. http://dx.doi.org/10.1016/j.cmpb.2021.106005.

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38

Dudykevych, V. B., V. V. Khoma, V. F. Chekurin, Y. V. Khoma, and D. V. Sabodashko. "ECG SIGNALS NORMALIZATION FOR SYSTEMS OF BIOMETRIC IDENTIFICATION." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 4, no. 1 (2019): 49–56. http://dx.doi.org/10.32838/2663-5941/2019.4-1/10.

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39

Bugdol, Marcin D., and Andrzej W. Mitas. "Multimodal biometric system combining ECG and sound signals." Pattern Recognition Letters 38 (March 2014): 107–12. http://dx.doi.org/10.1016/j.patrec.2013.11.014.

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40

Patro, Kiran Kumar, and P. Rajesh Kumar. "Effective Feature Extraction of ECG for Biometric Application." Procedia Computer Science 115 (2017): 296–306. http://dx.doi.org/10.1016/j.procs.2017.09.138.

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41

Sidek, Khairul Azami, Vu Mai, and Ibrahim Khalil. "Data mining in mobile ECG based biometric identification." Journal of Network and Computer Applications 44 (September 2014): 83–91. http://dx.doi.org/10.1016/j.jnca.2014.04.008.

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42

Merone, Mario, Paolo Soda, Mario Sansone, and Carlo Sansone. "ECG databases for biometric systems: A systematic review." Expert Systems with Applications 67 (January 2017): 189–202. http://dx.doi.org/10.1016/j.eswa.2016.09.030.

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43

Porée, Fabienne, Gaëlle Kervio, and Guy Carrault. "ECG biometric analysis in different physiological recording conditions." Signal, Image and Video Processing 10, no. 2 (December 30, 2014): 267–76. http://dx.doi.org/10.1007/s11760-014-0737-1.

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44

Sufi, Fahim, Ibrahim Khalil, and Ibrahim Habib. "Polynomial distance measurement for ECG based biometric authentication." Security and Communication Networks 3, no. 4 (July 2010): 303–19. http://dx.doi.org/10.1002/sec.76.

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45

Ramos, Mariana S., João M. Carvalho, Armando J. Pinho, and Susana Brás. "On the Impact of the Data Acquisition Protocol on ECG Biometric Identification." Sensors 21, no. 14 (July 7, 2021): 4645. http://dx.doi.org/10.3390/s21144645.

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Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

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46

Safie S.I. "New Heart Features for More Effective Human Identification." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 08 (June 28, 2022): 127–41. http://dx.doi.org/10.3991/ijoe.v18i08.31317.

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Biometric verification is a process to authenticate whether the subject is what it claims to be, based on the characteristics of the human body. These characteristics must meet seven (7) conditions to enable them to be used in a practical biometric system. These conditions are namely distinctiveness, performance, collectability, acceptability, universality, circumvention, and permanence. Electrocardiogram (ECG) is a human body characteristic measured from the heart that meets these seven conditions. Choosing the correct features from the ECG is important to get a high authentication rate. This paper proposed a new algorithm known as Bipolar Slope Feature (BSF) for ECG features selection. It is developed based on the relationship of slopes between several locations in a complete ECG cycle. The Receiver Operating Characteristic (ROC) curve is used to measure the effectiveness of this technique for the application of biometric verification.

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47

Alhayajneh, Abdullah, Alessandro Baccarini, Gary Weiss, Thaier Hayajneh, and Aydin Farajidavar. "Biometric Authentication and Verification for Medical Cyber Physical Systems." Electronics 7, no. 12 (December 14, 2018): 436. http://dx.doi.org/10.3390/electronics7120436.

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A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g., ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

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48

Islam, Md Saiful, Haikel Alhichri, Yakoub Bazi, Nassim Ammour, Naif Alajlan, and Rami M. Jomaa. "Heartprint: A Dataset of Multisession ECG Signal with Long Interval Captured from Fingers for Biometric Recognition." Data 7, no. 10 (October 21, 2022): 141. http://dx.doi.org/10.3390/data7100141.

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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.

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49

Kim, Junmo, Geunbo Yang, Juhyeong Kim, Seungmin Lee, Ko Keun Kim, and Cheolsoo Park. "Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning." Sensors 21, no. 5 (February 24, 2021): 1568. http://dx.doi.org/10.3390/s21051568.

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Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

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50

Wang, Di, Yujuan Si, Weiyi Yang, Gong Zhang, and Tong Liu. "A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification." Applied Sciences 9, no. 1 (January 8, 2019): 201. http://dx.doi.org/10.3390/app9010201.

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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).

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