What does this research mean for the field?

Convolutional Neural Network (CNN) models using raw off-the-person ECG data achieve high accuracy and reduced Equal Error Rate for biometric identification, demonstrating better performance compared to CNN-LSTM models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

March 1, 2019

CNN based Off-the-Person ECG Biometrics

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Why the study?

ECG-based biometric systems are typically less accurate than other physiological traits, though ECG offers uniqueness, specificity, and unidimensional characteristics.

Population

Raw off-the-person ECG biometric data

Comparison

Different layers of CNN vs CNN-LSTM model

Authors

MMM.A Anief MuhammedAmrita Vishwa Vidyapeetham JAJ AravinthAmrita Vishwa Vidyapeetham

Discussion

Member takes

Implication

May advance ECG biometric security systems; leaves open prospective clinical validation.

Structured PICO

Population

Raw ECG biometric data (off-the-person category)

Intervention

Convolutional Neural Network (CNN) models

Comparator

CNN-LSTM model

Outcome

Accuracy and Equal Error Rate (EER)

CNN models demonstrate improved accuracy and reduced Equal Error Rate for off-the-person ECG-based biometric security compared to CNN-LSTM models.

Cite This Study

Muhammed et al. (2019) studied this question.

synapsesocial.com/papers/6a1d278343708a372d5dd1f1 https://doi.org/10.1109/wispnet45539.2019.9032813

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CNN based Off-the-Person ECG Biometrics

Discussion

Member takes

Implication

Structured PICO

Cite This Study

CNN based Off-the-Person ECG Biometrics

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider