Key result
Time-domain ECG feature extraction achieves ~96% biometric identification accuracy in initial testing.
ECG signals can be used for biometric identification by extracting features in the time domain, offering a robust security modality.
Preliminary ECG biometrics feasible in small samples; leaves open validation in larger, diverse cohorts before adoption.
The Electrocardiogram (ECG) is the latest modality in field of Biometric identification. The electrocardiogram exhibit some unique cardiac characteristics from individuals, which encourages us for biometric application of ECG signal, robust nature of ECG against falsification makes it more safe for security systems, because it extend ultimate security in every situations. In this study, ECG biometric identification that is Feature extraction of ECG in time domain is used for biometric identification.
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Sunil Kumar (2018) studied Biometric identification (n=27). ECG feature extraction and linear classification was evaluated on Biometric identification accuracy. An ECG biometric identification method using time-domain feature extraction and a linear classifier achieved an average identification accuracy of 95.76% in an initial test of 7 subjects.
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