Comparison of Wavelet and Filtering Techniques for Denoising ECG Signal

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dc.contributor.authorSaminu, Sani
dc.date.accessioned2022-01-10T10:30:24Z
dc.date.available2022-01-10T10:30:24Z
dc.date.issued2014
dc.description.abstractA proper processing of biomedical signals enhances their physiological and clinical information because they carry vital information about the behaviour of the living systems under study. With the analysis of the Electrocardiogram (ECG) signal it may be possible to predict heart problems and play an important role in diagnosis process or monitor patient recovery after a heart intervention. The quality of this signal is degraded mainly by many sources of noise such as power line interference (PLI), baseline drift, muscle contraction noise etc. Present work deals with the design of filter banks based on the discrete wavelet transform (DWT) as well as design of low pass Chebyshev Type I and Butterworth filters using FDA tool in MATLAB environment. De-noised ECG signal is compared with original signal using Mean Square Error (MSE). Results show that denoising schemes involving wavelet domains are able to reduce noise from ECG signals more accurately and consistently with mse of 0.0012 in comparison to noise reduction algorithms in filtering technique which has mse of 0.0799 and 0.1814 for Chebyshev and Butterworth filters respectively.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7278
dc.language.isoenen_US
dc.publisherFaculty of Engineering, Bayero University Kano, Nigeriaen_US
dc.subjectECGen_US
dc.subjectDWTen_US
dc.subjectButterworthen_US
dc.subjectChebysheven_US
dc.subjectMSEen_US
dc.titleComparison of Wavelet and Filtering Techniques for Denoising ECG Signalen_US
dc.typeArticleen_US
dc.typePresentationen_US

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