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January 1, 2021IEEE Transactions on Instrumentation and Measurement

Collaborative-Set Measurement for ECG-Based Human Identification

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Why the study?

Conventional distance measures for ECG-based human identification rely on independent data samples, leaving them vulnerable to data distribution variations and bias caused by noisy artifacts.

Population

ECG data from the public DREAMER database

Comparison

Collaborative-Set Measurement vs conventional sample-level distance measures

Design

Algorithm development and validation study

Authors

WLWei LiZZZhen ZhangBHBowen Hou

Discussion

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Member takes

Overview

May improve ECG biometric robustness; leaves open clinical validation before adoption.

Structured PICO

P
Population
ECG data from the public challenging database, DREAMER
I
Intervention
Collaborative-Set Measurement (CSM) method
O
Outcome
Identification accuracy

The Collaborative-Set Measurement method enhances ECG-based human identification accuracy to 91.30% by utilizing multiple-set bundles to overcome noisy artifacts.

Cite This Study

Li et al. (2021) studied this question.

synapsesocial.com/papers/6a71f04926770c2b8de147fchttps://doi.org/10.1109/tim.2021.3083556
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