Key result
An SVM-based approach using temporal, amplitude, and morphological ECG features improved biometric recognition efficiency in healthy subjects and those with arrhythmia.
An SVM-based machine learning approach using temporal, amplitude, and morphological ECG features can effectively perform biometric personal identification.
ECG biometrics may aid identification in arrhythmia; leaves open prospective clinical validation.
This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty‐one features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT‐BIH Arrhythmia and MIT‐BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia.
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Rezgui et al. (2016) studied Healthy human subjects and those with arrhythmia. ECG biometric recognition using SVM-based approach was evaluated on Recognition performance. An SVM-based approach using temporal, amplitude, and morphological ECG features improved biometric recognition efficiency in healthy subjects and those with arrhythmia.
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