ECG-based biometric under different psychological stress states

Zhou, Ruishi; Wang, Chenshuo; Zhang, Pengfei; Chen, Xianxiang; Du, Lidong; Wang, Peng; Zhao, Zongci; Du, Mingyan; Fang, Zhen

doi:10.1016/j.cmpb.2021.106005

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…Fingerprint and facial patterns are physically exposed and prone to external attacks. ECG-based authentication systems, however, are difficult to deceive as the underlying biometric features of heart electrical activity are concealed [23].…”

Section: Ecg-based Biometric Authentication and Identificationmentioning

confidence: 99%

“…Several studies have aimed to improve the robustness of ECGbased authentication systems to intra-subject variability by cluster analysis. The idea is to partition the subject's ECG (or heartbeats) into clusters, when the subject is under different mental or physical conditions, and use information about the clusters, such as the center of the clusters, as an additional feature for the supervised learning method performing the authentication [20]- [23]. Most studies in this application use their collected ECG dataset recorded under different levels of emotion or stress.…”

Section: Ecg-based Biometric Authentication and Identificationmentioning

confidence: 99%

“…In addition to their application as a part of a broader knowledge discovery system, clustering techniques, in particular deep learning-based unsupervised methods, such as autoencoders [13], [14] and generative adversarial networks [15], have also been employed to overcome some challenges facing ECG supervised learning systems by resolving the imbalanced data problem [16] and low-level automation of patient-specific ECG classifiers [17]- [19]. Moreover, ECG clustering has been utilized in biometric authentication [20]- [23], ECG segmentation [24], [25], and fetal ECG extraction from abdominal ECG [26].…”

mentioning

confidence: 99%

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Unsupervised ECG Analysis: A Review

Nezamabadi

Sardaripour

Haghi

et al. 2023

IEEE Rev. Biomed. Eng.

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Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the large amount of data produced daily by cardiac monitors. As the number of abnormal ECG samples with cardiologist-supplied labels required to train supervised machine learning models is limited, there is a growing need for unsupervised learning methods for ECG analysis. Unsupervised learning aims to partition ECG samples into distinct abnormality classes without cardiologistsupplied labels-a process referred to as ECG clustering. In addition to abnormality detection, ECG clustering has recently discovered inter and intra-individual patterns that reveal valuable information about the whole body and mind, such as emotions, mental disorders, and metabolic levels. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. While several reviews exist on supervised ECG systems, a comprehensive review of unsupervised ECG analysis techniques is still lacking. This study reviews ECG clustering techniques developed mainly in the last decade. The focus will be on recent machine learning and deep learning algorithms and their practical applications. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application.

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Section: Ecg-based Biometric Authentication and Identificationmentioning

confidence: 99%

Section: Ecg-based Biometric Authentication and Identificationmentioning

confidence: 99%

mentioning

confidence: 99%

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Unsupervised ECG Analysis: A Review

Nezamabadi

Sardaripour

Haghi

et al. 2023

IEEE Rev. Biomed. Eng.

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“…Thereby, some researchers have investigated the impact of emotions on the identification of individuals [ 73 ]. Zhou et al [ 74 ] proposed a method of ECG biometrics using signals acquired under different stress levels, achieving an average recognition rate of 95%. Li et al [ 18 ] used the public database DREAMER, in which the ECG signals acquired from wearable devices are disturbed by the physiological noises from emotional fluctuations induced by different stimuli.…”

Section: Ecg Acquisition and Databasesmentioning

confidence: 99%

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

Pereira

Conceição

Sencadas

et al. 2023

Sensors

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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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“…e former is closer to reality and more effective in analyzing psychological stress. At present, there are many researchers in the world to collect pressure signals in actual work and life, but this method requires greater labor intensity, material and financial resources, and requires experimental equipment [20]. In contrast, the latter is more convenient and uses laboratory equipment and materials to achieve psychological stress induction, which not only has reliable data, but also has less interference in the collection process, providing help for the identification and research of psychological stress [21].…”

Section: Stress-induced Experimental Protocol Designmentioning

confidence: 99%

A Particle Swarm Algorithm-Guided Psychological Stress Analysis to ECG Signal Collecting

Peng

2022

Mobile Information Systems

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In recent years, effective recognition and accurate assessment of psychological stress have been the focus of research. Because of the objectivity and authenticity of physiological signals, psychological stress recognition from physiological signals has become an important research content in the field of psychological stress recognition. As an important physiological signal, electrocardiogram has been proved to contain reliable physiological response to psychological stress. This paper designs a psychological stress analysis algorithm based on particle swarm optimization (PSO). The wavelet transform algorithm was used to filter and detect the ECG signal. RR interval was calculated from the detected R wave to obtain the ECG signal. An improved particle swarm optimization (PSO) algorithm was proposed, which introduced a particle swarm optimization model with contraction factor to eliminate the speed limit and realize the detection of psychological stress. Experimental results show that the recognition rate of the improved particle swarm optimization algorithm is significantly higher than that of the traditional method, which shows the effectiveness of the algorithm. On the one hand, the research of this paper has optimized the algorithm, which has theoretical significance; on the other hand, it can provide reference for the real psychological stress test, which has practical significance.

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ECG-based biometric under different psychological stress states

Cited by 8 publications

References 17 publications

Unsupervised ECG Analysis: A Review

Unsupervised ECG Analysis: A Review

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

A Particle Swarm Algorithm-Guided Psychological Stress Analysis to ECG Signal Collecting

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