Recurrent neural networks achieved nearly 100% classification accuracy for ECG-based identification and reduced the equal error rate for authentication to nearly 0% with 80% training data.
RNNs offer a highly accurate approach for ECG-based biometric identification and authentication without requiring manual feature extraction.
In this paper, we propose the use of recurrent neural networks (RNNs) to develop an effective solution to two problems in electrocardiogram (ECG)-based biometrics: identification/classification and authentication. Different RNN architectures with various parameter settings were evaluated, including traditional, long short-term memory (LSTM), gated recurrent unit (GRU), unidirectional, and bidirectional networks. Unlike many existing methods, the RNN-based method does not require any feature extraction. The method was evaluated using two publicly available datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). For the identification problem, nearly 100% classification accuracy on the ECG-ID dataset was achieved, and similar results were observed for the MITDB dataset. For the authentication problem, an RNN was trained and the hidden state at the final time step was extracted to make a decision. We evaluated the effect of the training size on the equal error rate (EER), and showed that the EER drops from approximately 3.5% to nearly 0% as we increased the percentage of subjects used for training from approximately 15% to 80%.
Salloum et al. (Wed,) conducted a other in ECG-based biometrics. Recurrent neural networks (RNNs) was evaluated on Classification accuracy (identification) and equal error rate (authentication). Recurrent neural networks achieved nearly 100% classification accuracy for ECG-based identification and reduced the equal error rate for authentication to nearly 0% with 80% training data.
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