Key result
An ECG biometric system using eigenbeat features achieves up to ~96% individual recognition accuracy.
An ECG-based biometric system utilizing eigenbeat features can achieve high recognition accuracy for individual identification across both normal and arrhythmic subjects.
Supports ECG biometric development for identification; hypothesis-generating and requires prospective validation before clinical adoption.
The authors of this paper present a new method to characterize the electrocardiogram (ECG) for individual identification. We propose an ECG biometric system which is insensitive to noise signals and muscle flexure. The method utilizes the principal of linearly projecting the heartbeat features into a subspace of lower dimension using an orthogonal basis that represents the most significant features to distinguish the individuals. The performance of the proposed biometric system is evaluated on the subjects of both health statuses such as the ECG recordings of MIT-BIH Arrhythmia database and the ECG recordings of normal subjects prepared at IIT(BHU). The result demonstrates that the derived eigenbeat features from proposed ECG characterization perform better and achieve the recognition accuracy of 91.42% and 95.55% on the subjects of MIT-BIH Arrhythmia database and IIT(BHU) database, respectively.
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Singh et al. (2013) studied Healthy subjects and subjects with arrhythmias. ECG biometric system using eigenbeat features was evaluated on Recognition accuracy. An ECG biometric system using eigenbeat features achieved recognition accuracies of 91.42% and 95.55% on the MIT-BIH Arrhythmia and IIT(BHU) databases, respectively.
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