A Machine Learning Technique for Person Identification using ECG Signals

This paper presents a machine learning technique for person identification using electrocardiograms (ECG). The proposed technique consists of four processes; namely, data acquisition, pre-processing, feature extraction, and classification. Data set were collected from the MIT-BIH Arrhythmia database working on 30 subjects using lead II (MLII) obtained by placing the electrodes on the chest. Second process concerns with the noise reduction in ECG by removing baseline drift, power line interference and high frequency noise. Feature extraction process was studied by using a non-fiducial approach based on auto correlation and discrete cosine transform (AC/DCT) .In the last process , artificial neural network (ANN) have been used to classify subjects with a classification accuracy of 97%. Key-Words: ECG Signals, Feature Extraction, Classification, Neural Network, Machine Learning

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