Design and analysis of second order IIR notch filters with double frequency initialization

Jeedella, J.; Ahamad, H. Al; Al-Mualla, M.E.; Noras, James M.

doi:10.1109/ieeegcc.2006.5686232

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…In order to diminish 50 Hz power line interference, a second-order IIR notch filter was applied [19]. Further, baseline wander of signals, whose frequency is less than 0.5 Hz, was eliminated with the help of mother wavelet Dau-bechies6 (db6) by decomposing the signal using low-pass and high-pass filters under the relevant bands.…”

Section: Noise Reductionmentioning

confidence: 99%

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Hafshejani

Mehridehnavi

Hajian

et al. 2021

IET Signal Processing

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Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12-lead electrocardiogram (ECG) waves including T-wave, Q-wave, and ST-segment. The main goal of this study is to represent vectorcardiography (VCG) as a complementary diagnostic tool of the ECG method to discriminate the various type of MI from normal cases. The system proposed in this study was analysed on the Physikalisch-Technische Bundesanstalt diagnostic ECG database and a recorded signal database for 80 MI and 52 healthy cases. Each record consists of 15 ECG and VCG signals. In this study, tridimensional morphological features were applied to the classification and regression tree (CART) and the feedforward neural network classifier. To classify MI cases from healthy control cases of our recorded database, classification and regression tree achieved the same results when VCG features or ECG features were applied with an accuracy of 99.4%, a sensitivity of 100%, and a specificity of 98.7%. Further, by using VCG Octant features with this current method, anterior-MI and inferior-MI were separated with an accuracy of 98.9%, a sensitivity of 98%, and a specificity of 100%. The outcomes prove that the VCG features performed more accurately than ECG features in MI localisation. K E Y W O R D S classification and regression tree (CART), electrocardiogram (ECG), feedforward neural network (FFNN), morphological features, myocardial infarction (MI), vectorcardiogram (VCG)This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Noise Reductionmentioning

confidence: 99%