Ecg Signal Analysis Using Advance Dsp Techniques Adaptive Wavelet Neural Network

Abstract

For patient care, monitoring and disease diagnosis, Electrocardiogram (ECG) is one of the newlinemost important human physiological parameter which carries many embedded information newlineabout human health and especially the working and wellbeing conditions of heart. newlineApplication of ECG measurement is also very suitable for cardiac and high blood pressure newlinepatient due to its non- invasive nature. It is the graphical recording of the time varying newlinevoltages generated by the myocardium due to bioelectric activities during the cardiac cycle newlineand representing the cyclic contraction and relaxation of the human heart muscles. Necessary newlineinformation about the electrophysiology of the heart diseases and ischemic changes to the newlineheart rhythm is provided by pure ECG signal. A cleaned ECG signal provides valuable newlineinformation about the functional aspects of the heart and cardiovascular system. Diagnosis of newlineheart diseases at an early stage can prolong human life span expectancy through appropriate newlinetreatment. Doctors find difficulties in analysing the long ECG records in short time and the newlinehuman eyes are also poorly suited to detect the continuously changing morphology of ECG newlinesignal. These difficulties can be overcome by powerful computer aided diagnosis (CAD) newlinesystem. The CAD system not only analyses the long ECG records and morphological changes newlinebut also provides other important features like beat detection, classification, feature newlineextractions, arrhythmia diagnosis etc. Abnormality occurred in cardiac beats of the ECG newlineshape is generally called arrhythmia. Arrhythmia is a common term for any cardiac disorder newlinethat differs from normal sinus rhythm. Automatic computer aided ECG signal analysis for newlinedetection of heart beat is difficult due to the large variation in morphological and temporal newlinecharacteristics of ECG waveforms of different patients as well as in the same patients. The newlinemain aim of my research work is to process and extract the useful information from the ECG newlinesignal for the automatic beat detection using advance digital signal processing