Effects of diseased ECG on the robustness of ECG biometric systems

Loong, Justin Leo Cheang; Swee, Sim Kok; Bear, Rosli; Subari, Khazaimatol S; Abdullah, Muhammad

doi:10.1109/iecbes.2010.5742250

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…8. When compared to the methods of Agrafioti et al [16] and Loong et al [15], the time to acquire the ECG is decreased approximately by three to five times. Though our proposed method of analysis uses smaller windows, an additional process to optimize the appropriate window size of each SB-ECG is required, as shown in Fig.…”

Section: Discussionmentioning

confidence: 94%

“…(iii) Further, the analysis of the accuracy of classification is focused on the robustness to ECG shape or HRV condition. Then the total accuracy of the results is compared with that of different method of feature extraction AC/DCT [15], because the AC/DCT method by Agrafioti, Hatzinakos, and others is more reliable and has been published in more than 10 articles. Thus, this paper will directly compare the classification results with the AC/DCT + LDA method under the same HRV-ECG condition that is adopted from the Bio-Pac system as reported in Section 5.1.…”

Section: Discussionmentioning

confidence: 99%

“…So far, few works have accounted for HRV due to psychological effects or cardiac irregularities. Loong et al [15] proposed the effects of diseased subjects on the recognition rate of an ECG biometric system. Ten healthy subjects with more than one record were used for determining the recognition rates.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When S w is nonsingular, the basis vectors sought in (10) correspond to all significant eigenvectors of (S −1 w S b ). For an input heartbeat Z , its FLD-based feature representation can be obtained simply by a linear projection in (15).…”

Section: Fisher Linear Discriminantmentioning

confidence: 99%

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Robustness study of ECG biometric identification in heart rate variability conditions

Pathoumvanh

Airphaiboon

Hamamoto

2014

IEEJ Trans Elec Electron Eng

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Electrocardiogram (ECG) has been actively proposed as an aliveness biometric for years. In realistic application, this type of biometrics still needs to be verified in some conditions related to the practical use. Therefore, we propose in this paper the study of two themes that concern its realistic application. First, the single beat of an electrocardiogram (SB‐ECG) is used as the ECG feature, which aims to decrease the time to acquire the ECG from the individual. Second, for the robustness study of ECG features to heart rate variability (HRV), we investigate the relation between the correct identification and the HRV conditions. The HRV‐ECG data is set up and collected by using the Bio‐Pac system. The overall processes of the proposed method are the following: Continuous wavelet transform (CWT) is used to analyze each SB‐ECG. Then the root‐mean square (RMS) value of total energy of the wavelet coefficients of each P, QRS, and T segment is calculated. Next, the Fisher linear discriminant analysis (FLDA) is applied to all sets of the RMS value for dimension reduction. Lastly, the normalized Euclidean distance is implemented as the classifier. The experimental results demonstrate that, with the proposed method, the classification achieves 97% accuracy and the robustness study achieves over 80% accuracy with HRV‐ECG change up to 20%. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Fisher Linear Discriminantmentioning

confidence: 99%

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Robustness study of ECG biometric identification in heart rate variability conditions

Pathoumvanh

Airphaiboon

Hamamoto

2014

IEEJ Trans Elec Electron Eng

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“…Sidek et al [ 80 ] also used three different databases containing various irregular heart states: MIT-BIH Arrhythmia database [ 34 ], MIT-BIH supraventricular arrhythmia, and Charles Sturt diabetes complication screening initiative, achieving accuracies of 96.7%, 96.4%, and 99.3% for each, respectively. Loong et al [ 81 ] showed that diseased ECG only reduced the recognition rate by less than 1% and, thus, the system is robust towards diseased ECG. Contrarily, Chiu et al [ 82 ] registered a drop of 19% between identifying normal subjects and subjects with arrhythmia (100% and 81%, respectively).…”

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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Well proven and commercially available biometric technologies versus electrocardiogram: A review

Appaji¹

2014

International Conference on Circuits, Communication, Control and Computing

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Effects of diseased ECG on the robustness of ECG biometric systems

Cited by 8 publications

References 17 publications

Robustness study of ECG biometric identification in heart rate variability conditions

Robustness study of ECG biometric identification in heart rate variability conditions

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

Well proven and commercially available biometric technologies versus electrocardiogram: A review

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