CLASSIFICATION BUNDLE BLOCK DETECTION USING MAGNITUDE SQUARED COHERENCE

This paper conveys a technique for the detection of Bundle Branch Block (BBB) ECG patterns using Magnitude Squared Coherence (MSC) function. The MSC function finds common frequencies between two signals and evaluate the similarity of the two signals. The ECG variation in BBB can observed through the changes in the ECG signal. MSC technique uses Welch method for calculating t h e PSD. For the detection of Normal and BBB beats, MSC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 98.5 percent. The data was collected from MIT/BIH arrhythmia database.

[1]  Leon Glass,et al.  A method for detection of atrial fibrillation using RR intervals , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[2]  Kristina M Ropella,et al.  Differentiation of atrial rhythms from the electrocardiogram with coherence spectra. , 2002, Journal of electrocardiology.

[3]  U. Snekhalatha,et al.  Classification of Right Bundle Branch Block and Left Bundle Branch Block Cardiac Arrhythmias Based on ECG Analysis , 2015 .

[4]  S Cerutti,et al.  Spectral and cross-spectral analysis of heart rate and arterial blood pressure variability signals. , 1986, Computers and biomedical research, an international journal.

[5]  Xiaodong Li,et al.  On the relationship of non-parametric methods for coherence function estimation , 2008, Signal Process..

[6]  Muhammad Ibn Ibrahimy,et al.  Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions , 2013 .

[7]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[8]  Binwei Weng,et al.  Atrial fibrillation detection using stationary wavelet transform analysis , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Agnieszka Kitlas Golińska,et al.  Coherence function in biomedical signal processing: a short review of applications in Neurology, Cardiology and Gynecology , 2011 .

[10]  Fethi Bereksi-Reguig,et al.  Automatic Classification of Heartbeats Using Wavelet Neural Network , 2012, Journal of Medical Systems.

[11]  Aslak Grinsted,et al.  Nonlinear Processes in Geophysics Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series , 2022 .

[12]  Malay Mitra,et al.  Cardiac Arrhythmia Classification Using Neural Networks with Selected Features , 2013 .

[13]  R. Gupta,et al.  A statistical approach for determination of time plane features from digitized ECG , 2011, Comput. Biol. Medicine.

[14]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[15]  Madhuchhanda Mitra,et al.  Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification , 2014, IEEE Transactions on Instrumentation and Measurement.