The Research Of ShR Signal Detection Based On The Surface ECG

Shockable Rhythm (ShR), which includes Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), is the main cause of Sudden Cardiac Death (SCD). Early defibrillation is the only effective way for treating VF. Automated External Defibrillator (AED) can increase the survival rate of SCD outside hospital. Moreover, the key step for AED to implement its function is to detect ShR quickly, accurately and automatically. ShR detection algorithm based on entropy, which has important theoretical and practical significance, has been studied.Different cardiac rhythms correspond to different level of system complexities, based on which ShR detection algorithm was studied, mainly involving approximate entropy, sample entropy and multiscale entropy. Through theoretical analysis, simulation experiment and practical application, the nonlinearity of signals in ventricular arrhythmia was analyzed, ShR denoising algorithm based on HHT and bispectrum analysis was studied, and ShR detection algorithm based on entropy for effectively distinguishing Ventricular Flutter (VFL), Asystole (ASYS) was also studied; meanwhile, which was compared with other algorithms based on complexity.Firstly, the nonstationarity of the signals in ventricular arrhythmia was studied. Based on the stationarity signals, the mean, variance, autocorrelation function and power spectrum estimation of ShR was studied, and nonstationarity of ShR was determined. Focusing on the nonstationarity of ShR, the frequency spectrum of ShR and its variation profile over time was analyzed by time-frequency analysis algorithm. Multiple time-frequency analysis algorithms were compared, from which HHT time-frequency algorithm has been found with the characteristic of high time-frequency aggregation, through which the relationship between frequency and time of ShR has been accurately summarized.Subsequently, based on HHT-bispectra analysis algorithm, de-noising processing to ShR signal was conducted. Firstly, decomposition and reconstruction in the time domain by EMD was implemented, the IMF (Intrinsic mode functions) needed was selected for reconstruction, and evaluation was conducted in accordance with SNR and RMSE, secondly, bispectra analysis was implemented in denoised signals; the denoised signals were evaluated by SPNR in bispectra domain, which is unavailable in previous algorithms. The study suggested that HHT-bispectra analysis algorithms using EMD (Empirical Mode Decomposition) reconstruction and bispectra analysis for avaliable signal frequency band achieved double denoising, which lay the foundation for accurately identifying the characteristic point in ECG in future.Different cardiac rhythms may correspond to different system complexities, so entropy, a nonlinear dynamic parameters used for measuring sequence complexity and as statistical quantification, was used for detecting ShR, which acquired good effect. ShR detection algorithm based on entropy including sample entropy and multiscale entropy was proposed, which can quickly and effectively detect ShR, based on verification by CUDB and VFDB database. In view of good effect in VFL and ASYS detection by ShR detection algorithm based on approximate entropy, and high detection rate to sinus rhythm based on standard deviation of standard slope absolute value, combining algorithm for instance, ShR detection algorithm based on standard deviation of standard slope absolute value-approximate entropy has been proposed; through verification by CUDB and VFDB database, it has demonstrated that combining algorithm has better detection effect than single algorithmFinally, the main context and innovation in this paper was summarized, inadequacy in study was proposed, and recommendation for further work was given.