October 15, 2019IEEE Transactions on Biometrics Behavior and Identity Science

A Novel Approach for ECG-Based Human Identification Using Spectral Correlation and Deep Learning

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Population

Normal and abnormal ECG signals from nine small and large scale ECG databases

Comparison

Proposed CNN approach using spectral correlation images vs state-of-art approaches

Design

Algorithm development and cross-validation study

Authors

SASara S. AbdeldayemWest Virginia University TBThirimachos BourlaiUniversity of Georgia

Discussion

Member takes

Implication

May facilitate ECG biometrics; leaves open prospective validation before any clinical use.

Structured PICO

Population

Nine small and large scale ECG databases encompassing both normal and abnormal ECG signals

Intervention

ECG-based human identification using spectral correlation images fed into convolutional neural network (CNN) architectures

Comparator

State-of-the-art ECG-based machine learning approaches

Outcome

Human identification accuracy, false acceptance rate, and false rejection rate

A novel deep learning approach using spectral correlation of ECG signals achieves high accuracy for human biometric identification without requiring fiducial point detection or noise removal.

Cite This Study

Abdeldayem et al. (2019) studied this question.

synapsesocial.com/papers/6a95394dd44cea3cdd829304 https://doi.org/10.1109/tbiom.2019.2947434

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October 15, 2019IEEE Transactions on Biometrics Behavior and Identity Science

A Novel Approach for ECG-Based Human Identification Using Spectral Correlation and Deep Learning

View Full Paper

Ask AI

Bookmark

Population

Normal and abnormal ECG signals from nine small and large scale ECG databases

Comparison

Proposed CNN approach using spectral correlation images vs state-of-art approaches

Design

Algorithm development and cross-validation study

Authors

SASara S. AbdeldayemWest Virginia University TBThirimachos BourlaiUniversity of Georgia

Discussion

Member takes

Implication

May facilitate ECG biometrics; leaves open prospective validation before any clinical use.

Structured PICO

Population

Nine small and large scale ECG databases encompassing both normal and abnormal ECG signals

Intervention

ECG-based human identification using spectral correlation images fed into convolutional neural network (CNN) architectures

Comparator

State-of-the-art ECG-based machine learning approaches

Outcome

Human identification accuracy, false acceptance rate, and false rejection rate

A novel deep learning approach using spectral correlation of ECG signals achieves high accuracy for human biometric identification without requiring fiducial point detection or noise removal.

Cite This Study

Abdeldayem et al. (2019) studied this question.

synapsesocial.com/papers/6a95394dd44cea3cdd829304 https://doi.org/10.1109/tbiom.2019.2947434

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