A two-stage ECG authentication algorithm utilizing a Residual Depthwise Separable Convolutional Neural Network achieved 100% accuracy when evaluated on 138 individuals across two databases.
Does a two-stage algorithm using RDSCNN improve the accuracy and processing speed of ECG authentication?
A novel two-stage algorithm using RDSCNN achieves 100% accuracy and fast processing speed for ECG-based biometric authentication.
The electrocardiogram (ECG) is relatively easy to acquire and has been used for reliable biometric authentication. Despite growing interest in ECG authentication, there are still two main problems that need to be tackled, i.e., the accuracy and processing speed. Therefore, this paper proposed a fast and accurate ECG authentication utilizing only two stages, i.e., ECG beat detection and classification. By minimizing time-consuming ECG signal pre-processing and feature extraction, our proposed two-stage algorithm can authenticate the ECG signal around 660 μs. Hamilton’s method was used for ECG beat detection, while the Residual Depthwise Separable Convolutional Neural Network (RDSCNN) algorithm was used for classification. It was found that between six and eight ECG beats were required for authentication of different databases. Results showed that our proposed algorithm achieved 100% accuracy when evaluated with 48 patients in the MIT-BIH database and 90 people in the ECG ID database. These results showed that our proposed algorithm outperformed other state-of-the-art methods.
Ihsanto et al. (Sat,) conducted a other in Biometric authentication (n=138). Two-stage algorithm using Hamilton's method and Residual Depthwise Separable Convolutional Neural Network (RDSCNN) vs. Other state-of-the-art methods was evaluated on Authentication accuracy. A two-stage ECG authentication algorithm utilizing a Residual Depthwise Separable Convolutional Neural Network achieved 100% accuracy when evaluated on 138 individuals across two databases.