Intruder recognition using ECG signal

The electrocardiogram (ECG) is becoming a promising technology for biometric human identification. Usually ECG is used for health measurements and this is useful for biometric applications to state that the subject under analysis is live. But an individual identification shouldn't require a classical ECG clinical analysis where several contacts are applied to the person to be identified. In literature, ECG biometric recognition is usually studied for the recognition of a subject within a group of known subjects. In this paper, a new approach is considered. The aim of our embedded wearable controller is to authorize a subject or to reject him, labeling as an intruder unknown to the system. The study used 40 healthy subjects: two authorized and 38 intruders. A one-lead ECG trace has been recorded from the wrists of subjects, features have been extracted using a combination of Autocorrelation and Discrete Cosine Transform (AC/DCT) and then classified using a Multilayer Perceptron. Results show that intruder recognition can be performed with a success rate equal to 100%