A Method Of ECG Signal Detection Based On Wavelet Analyse

Cardiac diseases are one of major diseases that endanger human's health. Analysis of ECG (Electrocardiogram) signal is an important means of clinical diagnosis of cardiac diseases. And it has been a research hotspot for many years at home and abroad that use computer to analyze ECG and diagnose cardiac diseases.Electrocardiogram is a pictorial representation of the electrical activity of heart beats. Because of the direct relationship between the ECG waveform and interval of the heart beats, it is possible that doctor can diagnose cardiac disease and monitor patient conditions from the unusual ECG waveforms.QRS complex detection is the base of automatic analysis of ECG, which is mostly the first step of analysis and affects the following steps greatly. The difficulties of QRS detection lie in two factors: firstly, the physiological variability of QRS complex. Secondly, the various types of noises that can present in the ECG, for instancethe power line noise, muscle noise, baseline drift.Using Multiresolution Wavelet Analysis decomposed the ECG signal onmultiscales, different frequencies of the signal were showed on different scales. Based on the wavelet transform, this work introduces an algorithm to detect QRS complex. In particular, the quadratic spline wavelet has been adopted. The thesis first reviews wavelet transform briefly, then develops a QRS detention algorithm, which is then tested by using the MIT-BIH arrhythmia database.The result of experiments shows that the performance of models based on Quadratic Spline Wavelet is superior, and it can detect QRS complex exactly.Based on the research work about interference in QRS signal, the proposed QRS detection algorithm in this paper can be a useful tool for medical personnel who are interested in using QRS information to explore their research work.