Analysis of ECG Signal for Measurement and Detection of S T QT Segments and T Wave Alternans

Abstract

Sudden Cardiac Arrest (SCA) or Sudden Cardiac Death (SCD) is one of the newlineprimary causes of cardiac mortality around the world. If the risk of the SCD can be newlinedetected at early stage, it is possible to save life of the patients, with appropriate newlinetreatment. The risk detection of the SCD is possible by analysis of the standard 12 newlinelead Electrocardiogram (ECG) signals. The variations in the morphology of the newlineECG, especially in the ST-T wave and QT segments is directly linked to the risk of newlineSCD and Ventricular Arrhythmias (VA) through various researches. Some of these newlinechanges are so subtle that, are not noticeable just by mere visual inspection of the newlineECG. Advance level computerized ECG algorithms are therefore required for this newlineanalysis. newlineIn this thesis, analysis of ST and QT segments is demonstrated with single newlineand multilead techniques. Objective is to improve the quantitative and qualitative newlineperformances of the available methods using multilead concept. Two noninvasive newlineSCD markers, T Wave Alternans (TWA) and QT interval are studied in detail. A newlinenovel Stationary Wavelet (SWT) algorithm is suggested for classification of MI newlineand healthy subjects. Also multilead QT interval analysis is performed with three newlinefrank leads X, Y and Z. Patients with various cardiac abnormalities and healthy newlinesubject s analysis is done with multilead and single lead methods. Thesis also newlineexplains the fetal QRS and QT interval measurement algorithms, as a result of newlineparticipation in the international level challenge Physionet/CinC 2013. Following newlineparagraphs gives brief explanation of the work presented in the thesis. newlineThe work starts with development of multilead TWA detection concept. newlineConsidering the limitations of the single lead ECG analysis being lead dependent a newlinenew multilead TWA detection is proposed. In this algorithm Principal Component newlineAnalysis (PCA) technique is used for transforming the alternans related newlineinformation from ST segment on a new signal called as derived lead. The algorithm newlineis validated with calibrated alternans records. The resu