Biometric human recognition system based on ECG

El_Rahman, Sahar A.

doi:10.1007/s11042-019-7152-0

Cited by 21 publications

(14 citation statements)

References 14 publications

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“…The QRS detection processing is achieved utilizing the Pan and Tompkins algorithm [ 58 ] which performs a competent process for extracting features of the QRS complex. The extracted features are entered into Linear Discriminant Analysis (LDA), Fuzzy Logic (FL), or Feature Map-Neural Network (SOM-NN) classifiers [ 17 ] to apply the features matching. Further details explaining the proposed technique are found in [ 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

A. El_Rahman,

Alluhaidan

2024

PLoS ONE

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In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

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Section: Methodsmentioning

confidence: 99%

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

A. El_Rahman,

Alluhaidan

2024

PLoS ONE

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“…Considering this, we acquire the heartbeats from ECG signals as the features via the procedures as below. At first, for the emotion-disturbed ECG signals from each subject, we employ the R-peak detection method and the Pan-Tompkins algorithm, to locate the R peaks [16,17,21,28,33,58]. Then, on the basis of the detected R peaks, we segment the signals into the heartbeats covering the QRS complexes in view of the normal range of human heart rate.…”

Section: Set Collectionmentioning

confidence: 99%

“…Therefore, classifier learning plays an important role in further enhancing the identification performance by optimising feature space. Typical classifiers primarily include the K-Nearest Neighbour, Linear Discriminant Analysis, Artificial Neural Network (ANN), Support Vector Machine (SVM), Set-Based Distance Measure and so forth [20][21][22][23]. Besides, the combination of SVM, AdaBoost and differential evolution algorithms also has great potential to enhance the feature performance for identification [24,25].…”

Section: Introductionmentioning

confidence: 99%

Turning waste into wealth: Person identification by emotion‐disturbed electrocardiogram

Fang

Zhu

et al. 2023

IET Biometrics

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The issue of electrocardiogram (ECG)‐based person identification has attracted intense research interests nowadays. Different than existing related researches that advocate accentuating useful information and attenuating noisy artefacts in sensor data processing, A novel strategy of ‘turning waste into wealth’ is proposed to exploit the new discriminative information from the relationship between noise disturbance and signal data for this issue. Specifically, the authors design a new and simple method, the Set‐Group Distance Measure, based on the suitable fusion of multiple minority‐based distance measurements, whose power has initially been discovered for the issue. This method takes advantage of the collaborative variation information from the relative relationship, which is named as ‘relative information’, between different types of emotional noise disturbances and ECG signal data, to tackle the problem of large intra‐class variation but small inter‐class difference during identification. Experimental results have demonstrated the reasonability, effectiveness, robustness, efficiency and practicability of the proposed method upon public benchmark databases. This proposal not only provides technological inspirations for the further study in ECG‐based person identification, but also shows a fresh feasible way to handle the noise‐signal relationship for more general topics of sensor data classification.

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“…In the works presented in [69] and [74] the fiducial points are used to perform user identification, therefore, preprocessing methods including the analysis and detection of fiducial points are needed. One of the algorithms that include signal processing with noise removal and the detection of the fiducial points is the well-known algorithm of Pan-and-Tompkins [75,76,77]. Other preprocessing techniques for EKG signals include wavelet transform such as [78] or even use the wavelet transform to process the signal and to detect the R peak as in [79,61,80].…”

Section: The Process Of Ekg Identificationmentioning

confidence: 99%

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Fuster-Barceló

Peris-López²,

Cámara³

2022

Neurocomputing

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Biometric human recognition system based on ECG

Cited by 21 publications

References 14 publications

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

Turning waste into wealth: Person identification by emotion‐disturbed electrocardiogram

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

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