Electrocardiogram Biometrics Recognition Menggunakan Artificial Neural Network

William Sim Jayapranata, Rolly Intan, Liliana Liliana


Research on biometrics recognition has become popular in the last two decades. Electrocardiogram signal is one among many data that can be used in biometrics recognition purposes. It is unique for each individual, easy to obtain, and hard to forge made electrocardiogram well suited for biometrics recognition. In this research, an identifier will be made using the electrocardiogram signal of each individual.

In this research, non-fiducial approach on MIT-BIH Arrhythmia Database from physionet with Artificial Neural Network as classifier was used. Non-sequential classifier offers lower computational complexity compared to sequential classifiers. Non-fiducial approach does not require feature extraction but a method of truncating the signal to each heartbeat is still required. Artificial Neural Network method uses neuron on each layer to classify digitalized electrocardiogram signal data.

Experiment result using our method achieved 98.886% accuracy using MIT-BIH Arrhythmia Database. This research demonstrates Artificial Neural Network method capability as non-sequential classifier to identify electrocardiogram with non-fiducial approach.


Machine Learning; Artificial Neural Network; Non-fiducial; Non-sequential; Electrocardiogram

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