  This paper proposes a cross wavelet transform based method for
Electrocardiogram signal analysis where parameters are identified from wavelet
cross spectrum and wavelet cross coherence of ECG patterns. Most of the ECG
analysing systems use explicit time plane features for cardiac pattern
classification. Application of this proposed technique for classification
eliminates the need for extraction of various explicit time plane features and
hence reduces the complexity of the system. The cross-correlation is the
measure of similarity between two waveforms or two time series and the cross
examination reveals localized similarities in time and scale. Parameters
extracted from Wavelet Cross Spectrum (WCS) and Wavelet Coherence (WCOH) is
used for classification. A pathologically varying pattern in QT zone of
inferior lead III shows the presence of Inferior Myocardial Infarction (IMI).
The Cross Wavelet Transform and Wavelet Coherence is used for the cross
examination of single normal and abnormal (IMI) beats. A normal template beat
is selected as the absolute normal pattern. Computation of the WCS and WCOH of
the selected normal template and various other normal and abnormal beats
reveals the existence of variation among patterns under study. The Wavelet
cross spectrum and Wavelet coherence of various ECG patterns shows
distinguishing characteristics over two specific regions R1 and R2, where R1 is
the QRS complex location and R2 is the T wave region. Parameters are identified
for classification of Type 1 IMI (non Q type, with ST elevation and attenuated
QRS complex) and Type 2 IMI (Q type MI with deep Q and inverted T) and normal
subjects. Accuracy of the proposed classification method is obtained as 99.43%
for normal and abnormal class and 88.5% and 87.02% for Type I and Type II
respectively.
