Algorithm Research On QRS Detection Of Ambulary ECG Automatic Analysis

The cardiac disease is one of the main diseases to harm human body now. ECG is an important method for the diagonosis in the cardiac disease in clinic. The exact automatic analysis for ECG signal is the key to the diagnosis in the cardiac disease, and which is also the problem many researchers pay a mount of attention to solve. Dissertation focuses on the further research on the algorithm of QRS complex detection based on the previous work.Firstly, the morphology and the characteristic of QRS complex in clinic are discussed. Then the difficulty of QRS complex detection in Ambulary ECG Automatic analysis is analysis, and the main handicaps in QRS complex detection are given.Secondly, various existed algothirm for QRS complex detection are categorized and summarized. Comparing the methods in detail, the problems are discussed in depth. And an algorithm of QRS complex detection based on wavlet transform is implemented, in which the RR interval is applied to estimate the positon of the next QRS complex. This will be the foundation for the new algorithm of QRS complex detection.Thirdly, QRS complex detection is the key part in the ECG automatic analysis. The accuracy of QRS position detected will affect the correctness and veracity of later analysis and signal extraction. In dissertation, a per-process method of QRS complex detection based on mathematical morphology and envelope extraction is implemented according to the research on the QRS complex morphology and noise in clinic, by which the noise, such as EMG, baseline movement, can be restrained effectively and QRS complex can be easily found by the detection rule.Fourthly, the estimate of QRS complex positon by RR interval is used in the algorithm. By the application of the estimate of QRS complex positon, the frequency of computing in the detection rule is greatly reduced and even high position veracity is reached in the assurance of detection precision.Finally, an integrated algorithm for QRS complex detection is implemented, which utilizes other technologies, such as RR interval and fuzzy reasoning, based on the mathematical moprphology and envelope.The complete set of MIT-BIH arrhythmia database is used to evaluate this algorithm, the gross error is 0.42% when the range of the signed point is reduced to 120ms. With the goodveracity, the analysis speed is also satisfying.