Analysis of ST-Segment Abnormalities in ECG using Signal Block Averaging Technique

Abstract

Abstract:The ischemic myocardium and coronary heart disease which occur due to abnormality in firing of the electrical impulses from the ventricles are causing an unanticipated death. This is most commonly referred to as Acute Myocardial Infarction (AMI). The value of ST-segment duration is not always easy to determine if the electrocardiograms (ECGs) are often spoiled by different types of artefacts. Occasionally it is not likely to recognize the exact end of the S wave and the accurate commencement of the T wave. Even if the Holter monitoring systems is useful to detect ST segment elevation or ST depression with good accuracy, there is no stringent technique of measuring an ischemic ST episode. This paper is intended to detect automatically ST-segment deviations and transient ST episodes which will help us in identification of Myocardial Infarction and also to categorize major cardiac arrhythmia. Our system has been tested and yielded an accuracy of 94.5% for the ST episode detection on the Long Term ST Database and 97.4% on MIT-BIH Arrhythmia Database.

Extraction and Analysis of the P Peak Morphology

“…This in turn activates the ventricles that contract and pump blood to the aorta and to the rest of the body. Finally, depolarization occurs followed by repolarization and this cycle is repeated [6]. The track of each heartbeat would consist of several waves/ peaks, segments, intervals and joints as a recurrent wave sequence as shown in the figure below in Fig 2 (d).…”

Classification of ST segment in ECG signals based on cross correlated supervised data

“…Normal, Ventricular, Atrial, Fusion, Right Bundle, and Left Bundle Branch Block beats by Discrete Wavelet Transformation (DWT), Windowing Technique and Slope Detection process [10]. ST segment abnormalities detection based on Signal Block Averaging Technique (SBAT) is also developed in [11]. They have developed an algorithm and applied SBAT for each ECG episode.…”