Clustering-based Evaluation Framework of Feature Extraction Approaches for ECG Biometric Authentication

In recent times, electrocardiogram signals have been leveraged for biometric verification. The efficacy of such authentication is reliant on the feature extraction from the electrocardiogram signals. A number of electrocardiogram feature extraction methods are currently available, but these methods may not be universally applicable in different dataset collection scenarios. To tackle this issue, this paper introduces a clustering-based framework to assess the feature extraction techniques for electrocardiogram biometrics. In this paper, the effectiveness of the framework is validated by using different electrocardiogram feature extraction techniques and different electrocardiogram databases. The framework provides important insights into electrocardiogram signal.

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IJCNN 2024: International Joint Conference on Neural Networks

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978-8-3503-5931-2

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