Abstract
In this paper, a non-fiducial approach using Wavelet Packet Decomposition (WPD) algorithm for repeated examination of solitary cable ECG used for individual identification 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 file is fed as an input to a machine learning classifier 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 classifier is applied by generating 100 trees. It is shown that the method is effective for quantifying the classification of arrhythmia ECG signals with accuracy of 92.62%.Keywords: Biometrics, ECG, MIT-BIH Arrhythmia Database, Random Forest, Wavelet Packet Decomposition
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
