English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a cross wavelet transform based method forElectrocardiogram signal analysis where parameters are identified from waveletcross spectrum and wavelet cross coherence of ECG patterns. Most of the ECGanalysing systems use explicit time plane features for cardiac patternclassification. Application of this proposed technique for classificationeliminates the need for extraction of various explicit time plane features andhence reduces the complexity of the system. The cross-correlation is themeasure of similarity between two waveforms or two time series and the crossexamination reveals localized similarities in time and scale. Parametersextracted from Wavelet Cross Spectrum (WCS) and Wavelet Coherence (WCOH) isused for classification. A pathologically varying pattern in QT zone ofinferior lead III shows the presence of Inferior Myocardial Infarction (IMI).The Cross Wavelet Transform and Wavelet Coherence is used for the crossexamination of single normal and abnormal (IMI) beats. A normal template beatis selected as the absolute normal pattern. Computation of the WCS and WCOH ofthe selected normal template and various other normal and abnormal beatsreveals the existence of variation among patterns under study. The Waveletcross spectrum and Wavelet coherence of various ECG patterns showsdistinguishing characteristics over two specific regions R1 and R2, where R1 isthe QRS complex location and R2 is the T wave region. Parameters are identifiedfor classification of Type 1 IMI (non Q type, with ST elevation and attenuatedQRS complex) and Type 2 IMI (Q type MI with deep Q and inverted T) and normalsubjects. 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 IIrespectively.

Classification of ST and Q Type MI variant using thresholding and neighbourhood estimation method after cross wavelet based analysis