The Detection Of Ventricular Waves Based On ARM Technology

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are catastrophic and life-threatening ventricular arrhythmia, which are prone to cause a sudden death. There is no sign before it. According to statistics by AHA, up to 30 percent of people died of a sudden death have no or never have heart disease. Almost all of them died outside of hospital. If the disease can be detected ahead and then treatment were carried out immediately, it can improve the opportunity of subsistence rate. Therefore, it is an important topic of forecasting a sudden death and life-threatening ventricular arrhythmia, which is right the main content of this paper.ECG (Electrocardiogram) signals are weak, nonlinear, non-stationary and random. The common amplitudes of them are just millivolt-high. Because of the direct contact of skin with ECG apparatus, there are a lot of noises in the collected signals, such as power-line interference, baseline-drift, muscle contraction (EMG), electrode contact noise, motion artifacts etc. Theses noises lead to a serious interference in analyzing and detecting ECG signals as well as in an effective treatment, thus a highly effective restraint of noises should be carried out firstly. Wavelet transform (WT) is a multi-resolution method based on time-frequency domain, which can be viewed as a camera adjusting its focus automatically. WT can extract and identify the tiny physiological signals from noises. It can not only improve the signal-noise ratio, but also achieve an excellent resolution when signals change suddenly. Therefore, WT is an effective tool in analyzing ECG signals. In this paper, coif4 wavelet is chosen to remove noises. Based on the typical soft-threshold and hard-threshold de-nosing algorithm, an algorithm of a tradeoff of the two thresholds is used. The simulating results show that coif4 wavelet is very suitable for ECG de-nosing. It can effectively get rid of the common noises, such as muscle contraction, power-line interference, baseline-drift and so on, and at the same time, it can effectively keep the useful ECG information. Recent studies have shown that the cardiac dynamics are complex and nonlinear. Thus the nonlinear dynamics or nonlinear mathematical models are considered to be suitable tools for analyzing ECG signals. This paper introduces a nonlinear technique called Hurst index algorithm for VT and VF discrimination. In brief, the Hurst index is defined in the multi-scale domain as a feature to quantify the nonlinear dynamics behavior of the ECG signals for detecting the life-threatening ventricular arrhythmia. In this paper, when studying the cardiac mechanism and state, it is not based on the extraction of the ECG features from the time sequences, but is in accordance with the changing ECG dynamic mechanism and dynamic features to uncover the changing trends of dynamic index from healthy NSR to VT, and to VF when the heart is broken and is prone to a sudden cardiac death. Thus, the variational dynamic index can supply a reliable reference to clinical ECG study. One typical method to detect life-threatening ventricular arrhythmia is Complex Measure algorithm, which detects VT and VF through quantifying ECG dynamic features in time domain. After a lot of simulations, it can be concluded that when the WL (Window Length) is equal to 7s, the Complex Measure algorithm can achieve 100% detecting accuracy in discriminating NSR, VT, and VF from each other, while using the Hurst Index algorithm, the WL just needs 5s. Furthermore, upon applying Hurst index algorithm and Complexity measure algorithm to the same ECG episodes, the Hurst index has a better real-time capability than the Complexity measure. Besides, the extracted VT in Complexity measure is monomorphic, while in this study the extracted VT contains both monomorphic and polymorphic waves. Due to the real-time capability and prevalence as well as reliability, the Hurst index has a better potential for clinical application than the Complexity measure.This study puts an emphasis on algorithm. The mainly used databases in this study are MIT-BIH Arrhythmia Database (MITDB),MIT-BIH Malignant Ventricular Ectopy Database (VFDB) and CU Ventricular Tachyarrhythmia Database (CUDB), which are all from a physiological signal database called PhysioBank. This physiological signal database is established by American MIT University, and known as standard and authoritative by the world.The embedded ARM microprocessor is the hottest technique at present. It has been widely used in every domain, such as industry control domain, consumed-electrical products domain, communication system domain, network system domain, wireless system domain and etc. It is gradually permeating everywhere in our daily life. The ARM technique has many advantages, for example, small volume, low power cost, low expense, high quality, excellent specificity, concise system and so on. Applying ARM microprocessor to the design of easy-taking ECG monitoring system can decrease the complexity of circuit, the volume of the system and improve the computing speed, storage capability as well as the monitoring quality. The easy-taking ECG monitoring system can detect the ventricular arrhythmia in time and give rise to an alarm at the same time. It possesses such traits as small volume, easy-taking, smart operations etc, and plays an important role in saving patients'life outside hospitals. Presently, more and more people become familiar with the easy-taking ECG monitoring system and more and more medical staffs carry on the study and improvement of easy-taking ECG monitoring system. This study suggests adopting Philips Company's LPC2368 as microprocessor and a discussion on the system structure based on LPC2368 is given in order to supply a hard platform for the Hurst Index algorithm and a reference for future study.