Visible to the public ECG Signal Classification Using Convolutional Neural Networks for Biometric Identification

TitleECG Signal Classification Using Convolutional Neural Networks for Biometric Identification
Publication TypeConference Paper
Year of Publication2021
AuthorsCordoș, Claudia, Mihail\u a, Laura, Faragó, Paul, Hintea, Sorin
Conference Name2021 44th International Conference on Telecommunications and Signal Processing (TSP)
KeywordsBiological system modeling, biometrics, biometrics (access control), classification, composability, convolutional neural network, Databases, ECG signal processing, Electrocardiography, Metrics, pattern classification, privacy, pubcrawl, resilience, Resiliency, Signal processing algorithms, signal processing security, Spectrogram, Telecommunications
AbstractThe latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.
DOI10.1109/TSP52935.2021.9522631
Citation Keycordos_ecg_2021