An efficient method of biometric matching using interpolated ECG data

In this paper, a person identification method using electrocardiogram (ECG) is presented based on cubic spline interpolation method. Three different databases with two different sampling rates containing 36 ECG recordings were used for development and evaluation. Each ECG recording is divided into two segments: a segment for enrolment, and a segment for recognition. The ECG features are extracted from both the training dataset and the test dataset for model development and identification. Two ECG biometric algorithms which are Cross Correlation (CC) and Percent Root-Mean-Square Deviation (PRD) were used for performance evaluation. Results of experiments confirmed that the template matching using interpolation method achieved better accuracy (up to 4.46%) than the existing method without interpolation when using ECG data with lower sampling rate.

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