Natural Exponential and Three-Dimensional Chaotic System.

Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two-dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three-dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics-related problem solving and expands the potential scope of multi-dimensional chaotic systems.

AFW extraction based on MCA.

This paper improves the learning dictionary construction method for morphological component analysis (MCA) to separate the atrial and ventricular signals. The incoherence is added into the objective function to reduce the sparsity ratio between the atrial and ventricular dictionaries. By using the dictionaries, atrial and ventricular activities are separated from the location of the coefficients. We test the methods on both the synthetic and real atrial data. While extracting AFW from synthetic data, we use the Poisson relation as the measure. The result shows that we can obtain greater relation value using the method this paper presents than using the methods of ABS and PCA. We also conduct spectral analysis on AFW extracted from real atrial data.

Feature extraction from a novel ECG model for arrhythmia diagnosis.

Feature extraction is a crucial aspect of computer-aided arrhythmia diagnosis using an electrocardiogram (ECG). A location, width and magnitude (LWM) model is proposed for extracting each wave's features in the ECG. The model is a stream of Gaussian function in which three parameters (the expected value, variance and amplitude) are applied to approximate the P wave, QRS wave and T wave. Moreover, the features such as the P-Q intervals, S-T intervals, and so on are easily obtained. Then, a mixed approach is presented for estimating the parameters of a real ECG signal. To illustrate this model's associated advantages, the extracted parameters combined with R-R intervals are fed to three classifiers for arrhythmia diagnoses. Two kinds of arrhythmias, including the premature ventricular contraction (PVC) heartbeats and the atrial premature complexes (APC) heartbeats, are diagnosed from normal beats using the data from the MIT-BIH arrhythmia database. The results in this study demonstrate that using these parameters results in more accurate and universal arrhythmia diagnoses.