Abstract
Currently biometrics is widely used for security needs. In particular, the ECG that provides optimum security since it is more universal and difficult to be forged. This research presents a new approach for personal identification from electrocardiogram signals. After pre-processing, fiducial points were detected for each heartbeat. Then three types of features, namely temporal attributes, amplitude attributes and morphological descriptors, are extracted. Hidden Markov model was used for analysis of parameters and personal recognition. A combination between 21 features and 10 morphological parameters was performed in a one system in order to bring more significant improvement in terms of recognition. Results demonstrate that the proposed method is efficiently used to identify the normal and diseased subjects. In particular, the best identification rate of 99.10% is obtained for the subjects of MIT-BIH Normal Sinus Rhythm database whereas the subjects of MIT-BIH Arrhythmia database have led to a recognition accuracy of 98.39%.
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