A New ECG Identification Method Using Bayes´ Teorem

A human identification method using electrocardiogram (ECG) is presented based on Bayes´ theorem. A data base containing 502 ECG recordings are used for development and evaluation. Each ECG recording is divided into two segments: a segment for training, and a segment for performance evaluation. The ECG features are extracted from both the training dataset and the test dataset for model development and identification. Principal component analysis is used to reduce the dimension of feature variables. Classification method based Bayes´ theorem are deduced. Results of experiments confirmed that the classification based on Bayes´ theorem achieved better accuracy than the exiting method based on the Mahalanobis´ distance