Researching On The Electrocardiogram Signal Preprocessing Algorithm Based On Wavelet Transform And Its Android Implement

There are many portable ECG monitors on the market, but their operations are relatively complex.They generally use special system platform, can only use software developd by manufacturers.And the prices are expensive.lt is not suitable for the elderly to use alone indoors.Thus developing a household ECG real-time monitoring system is very meaningful. This thesis designed a model of ECG monitoring system based on the Android platform with the characteristics of simple operation and easy to use.This thesis first introduces the mechanism of ECG signal and noise interference, studies wavelet pretreatment algorithm according to the characteristics of the ECG signal.This thesis introduces four kinds of common methods to select the threshold value, and adopts the method of hard threshold and soft threshold to contrastively analyze the four kinds of methods.Then, this thesis proposes a new hierarchical threshold denoising algorithm.A simulation test was carried out whose result showes that the algorithm is a good way to filter the noise in the ECG signal. QRS complex detection algorithm is studied.A more practical R wave adaptive threshold detection algorithm is proposed, which can detect the singularity of signal utilizing the characteristics of wavelet modulus maxima. The algorithm can retain the complete information of QRS’ characteristic waveform.Finally, the ECG monitoring system based on wearable equipment is overall designed. The system hardware is mainly composed of intelligent bluetooth heart rate belt and tablet PC carrying of Android. Android client application is developd by using Java language. The system realizes the data transmission of intelligent terminal and bluetooth heart rate, the processing of ECG signal, real-time drawing display of ECG waveforms and heart rate, and ECG information storage based on the SQLite database.