The Real-time Classification And Detection Of Lowns Based On Cortex

Cardiac diseases are one of major diseases that endanger human's health, and ventricular arrhythmia is the most common diseases of cardiac diseases. For example: the most common cardiogram arrhythmia is premature ventricular contractions (PVC). PVC has been appeared or would appear in everyone's life, but most people is asymptomatic, it only be found in regular medical examinations. Even if one person has the symptom, the symptoms between different patients are rather large, and the relationship between clinical symptoms and prognosis is not parallel, PVC has different significance of prognosis in different heart "background". To deal with the different PVC correctly and give the treatment appropriately is very important, if not would cause iatrogenic symptoms and adverse consequences. So the classification and automatically detection of ventricular arrhythmia is not only a long-term problem, but also a practical clinical significance.We have completed three aspects as follows:First is preprocessing for ECG signals. We introduce the basic knowledge of ECG and different kinds of noises. And we remove baseline drift using wavelet construction algorithm, then, power line interference and electromyographical interference are removed using an improved threshold method to get a clean ECG signal.The second is detecting the QRS wave of ECG signal and extracting the related parameters. we give a brief introduction of wavelet transform, the correlation between wavelet transform' maximum or zero points and singular points of signal. We detect the characteristic points of the ECG signal, including R peak value, R peak position, QRS complex starting and ending points. With these characteristic points, we calculate the width of the QRS complex.The third is researching for Lowns, include it's classification standards and detection. We introduce the classification of common arrhythmia and point out that the most common classification of ventricular arrhythmia is Lowns. And then the detection algorithm of typical ventricular arrhythmia is given, we also given the simulation results.The fourth is the introduction for implementation scheme of hardware. We mainly introduces the design principles of hardware, and according to some characteristics of the microprocessor we choice the ARM Cortex-M3 for our core data processing module. In this part we introduce the prototype system and software development environment. At the same time, the implementation scheme of Lowns classification detection technique based on Cortex and Lowns detection programming flowchart are introduced.