ECG Biometrics Using Spectrograms and Deep Neural Networks
Nuno Bento, David Belo, Hugo Gambôa
Abstract
The Electrocardiogram (ECG) is considered as a physiological signature and has previously been used for biometric purposes. The contamination of the signal due to noise adds undesired intra-variability in the ECG signals, creating the need for more robust biometric systems (BSs). With the increase of interest in the application of Deep Neural Networks (DNN) to the medical field, new solutions are also being explored in the identification and authentication of individuals. The proposed architecture exploits the potential of Convolutional Neural Networks (CNN) to identify healthy subjects using temporal frequency analysis, i.e. spectrograms.
Topics & Concepts
SpectrogramBiometricsComputer scienceConvolutional neural networkNoise (video)Artificial intelligenceAuthentication (law)Pattern recognition (psychology)Speech recognitionIdentification (biology)Deep learningField (mathematics)Artificial neural networkImage (mathematics)Computer securityBiologyMathematicsPure mathematicsBotanyECG Monitoring and AnalysisEEG and Brain-Computer InterfacesNon-Invasive Vital Sign Monitoring