2014 · 20 citations · 12 references
ComputingBiomedical EngineeringElectrical EngineeringAuthenticationBiomedical Signal ProcessingFeature EngineeringBiometric AuthenticationElectrocardiogram AnalysisElectrocardiographyFeature ExtractionPattern RecognitionVital SignsBiomedical Signal FeaturesBiometric Feature ExtractionBiometric RecognitionEcg Feature ExtractionEcg Signal PreprocessingElectrocardiogram Feature ExtractionPhysiological Biometrics
Over the last few years, the Electrocardiogram (ECG) was introduced as a powerful biometric modality for human authentication. Indeed, ECG has some characteristics specific to each individual. In this paper we present an authentication system based on the ECG signal. We are particularly interested in the feature extraction step where we propose new approach based on the slopes and the angles of the ECG signal. The neural network is used for the classification step. The results have been validated on a database related to 100 persons. We recorded a recognition rate (RR) equals 96.44% which is an encouraging result relative to the size of the database.
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