Key result
A 1-D convolutional neural network using ECG signals under arrhythmia conditions achieved an F1-score accuracy of 92% and a loss of 0.25 for biometrics identification.
Why the study?
Fingerprint biometrics are externally exposed and easily forged, prompting investigation into ECG signals under arrhythmia conditions as an alternative modality for biometric systems.
A 1-D CNN model using ECG signals under arrhythmia conditions achieved 92% accuracy for biometric identification, demonstrating feasibility for real-world applications.
Answers are grounded in this paper and cite their sources.
ECG biometrics in arrhythmia needs prospective validation; leaves open clinical translation.
Applications of Biometrics technology are extremely popular today, ranging from access control to automation. Fingerprint is the oldest and the most widely used biometrics technology. However, its key features are externally exposed which make it tend to be easily forged. This study investigates the possibility of electrocardiogram (ECG) signal as an alternative modality for biometrics systems. Besides that, the study is conducted using the ECG database under arrhythmia conditions to accommodate the real-world application since arrhythmia exists in large-scale world populations. In this study, a total of 8,972 datasets from 47 subjects were modeled using a machine learning technique (i.e., one-dimensional convolution neural network or 1-D CNN). The results showed that the accuracy (F1-score) of 92% and 0.25 of loss was achieved. Furthermore, we prove that the proposed model is a good fitting based on the visualization plot of the train-test. These findings show that the proposed model is reasonable enough for an ECG-based biometrics system though it's not the best in the literature.
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Yuniarti et al. (2022) studied Arrhythmia (n=47). 1-D Convolutional Neural Network (1-D CNN) using ECG signals was evaluated on Accuracy (F1-score). A 1-D convolutional neural network using ECG signals under arrhythmia conditions achieved an F1-score accuracy of 92% and a loss of 0.25 for biometrics identification.
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