Abstract:
In this thesis, a non- ducial approach based on wavelet packet decomposition
(WPD) algorithm for repeated examination of solitary lead electrocardiogram
(ECG) for individual identi cation is planned and tested. Multiple
samples of ECG wave are extracted considering R-peak as a reference and
WPD algorithm is applied for feature extraction. This feature le is fed as
an input to a machine learning classi er i.e. random forest in order to classify
the individuals. In this work, records from publicly available MIT/BIH
arrhythmia dataset have been utilized to evaluate the proposed system. Best
result relies on third level of wavelet decomposition using Daubechies wavelet
to analyze the signal. Furthermore, ranker search method is used in conjunction
with relief attribute evaluator for feature selection and random forest
classi er is applied by generating 100 trees. It is shown that the method is
e ective for quantifying the classi cation of arrhythmia ECG signals with
accuracy of 92.62 %.
