Key result
Multi-cycle ECG waveforms achieve 100% biometric identification accuracy using finger measurements.
Why the study?
Can multi-cycle ECG waveform patterns be used for accurate individual biometric identification?
Population
55 subjects for wrist ECG measurement and 20 subjects for finger ECG measurement
Design
Other
Authors
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Demonstrates proof-of-concept for rapid ECG-based personal authentication; leaves open the real.
Can multi-cycle ECG waveform patterns be used for accurate individual biometric identification?
Multi-cycle ECG waveform patterns can be used for highly accurate individual biometric identification, particularly when measured on the fingers.
Lee et al. (2018) studied this question. Multi-cycle ECG waveform pattern matching was evaluated on Biometric identification accuracy. Biometric identification using multi-cycle ECG waveform patterns achieved up to 93.3% accuracy with wrist measurements and 100% accuracy with finger measurements using three heartbeats.
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