Research On T Waves Alternans Detection Based On Gibbs Sampler And CCA

ECG automatic diagnosis technology in cardiovascular disease occupying the important status in the diagnosis of disease. We found T wave alternating in the ECG signal electric is an important predictors in the prevention of sudden cardiac death. The key technology of T-wave alternating is the hot spot in the field of signal processing and the medical profession.This thesis introduces the general system model of the TWA test, mainly including the preprocessing module, T wave extraction and alignment module, and the TWA testing module. In this thesis, We mainly focus on the T wave extraction and alignment, and TWA detection method these two aspects and proposed new algorithms. The following is the main work in this thesis:(1) T waves extraction and alignment. This report studied a Bayesian sampling algorithm performing T-wave delineationand waveform estimation.Firstly, We introduce the ahierarchical Bayesianmodel for T-wave delineation.Using the conjugate prior distribution, get the posterior distribution of bayesian model unknown parameters (position of T wave, amplitude, waveform),The derivation of a GS allowing one to generate samples distributed according to the posterior distribution associated to the previous hierarchical bayesian model. Estimation of the T-wave peak locations, amplitudes and waveform coefficientsbased on the generated samples using MMSE estimators. T wave can be accurately extracted. After extraction the length of each T wave is different, this makes TWA dectection unconveniently, so we use interpolation method to align the T wave matrix.(2) Analysis of TWA. Firstly we described various testing methods which proposed by others. Then we put forward a cca method for TWA testing method. Ecg signal is divided into odd and even heart, using the canonical correlation to find the biggest relevance vector analysis for two groups of heart beat. This method greatly improves the accuracy of detection.(3)Software implementation. Mainly introduced the ecg intelligent analysis software, the software implemente ecg intelligent algorithm in this thesis. Based on c# and matlab programming, using SQL Server 2000 database to store user information, use the file system to store data.Compared with the traditional methods, the TWA testing methods proposed in this thesis have certain features and benefits. T wave extraction method in this thesis ues the T wave shape information, but common extraction method not use. Then we can accurately various forms of T wave. In terms of TWA testing using the method of canonical correlation analysis using correlation of the signal and improve the accuracy of the estimation. The algorithm has better antinoise performance, high accuracy and with different forms of the TWA have good test results.