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An ECG-based human identification method using Bayes' theorem achieved better classification accuracy compared to the existing method based on Mahalanobis' distance.
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An ECG-based human identification method using Bayes' theorem provides better accuracy than traditional Mahalanobis' distance methods.
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
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Zhang et al. (2006) studied Human identification (n=502). Classification method based on Bayes' theorem vs. Classification method based on Mahalanobis' distance was evaluated on Classification accuracy. An ECG-based human identification method using Bayes' theorem achieved better classification accuracy compared to the existing method based on Mahalanobis' distance.
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