This paper investigates the applicability of high order statistical autoregressive (AR-HOS) modeling method in analyzing biomedical signals. The autoregressive (AR) method using linear prediction and AR-HOS method using cumulants are applied on normal and pathological heart sound signals. It is found that the AR-HOS modeling a signal produce more accurate and higher resolution spectrum than AR ...

in this paper, the method of differentiating asthmatic and non-asthmatic patients using the  frequency analysis of  capnogram  signals is presented.  previously, manual study on capnogram signal has been conducted  by several researchers. all past researches showed significant correlation between capnogram signals and asthmatic patients. however all of them are just manual study conducted throu...

In myoelectric prostheses design, it is normally assumed that the necessary control information can be extracted from the surface myoelectric signals. In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive (AR) model coefficients are generally considered an efficient and robust feature set. However, no formal statistical methodologies or tests are repo...

In this paper a fuzzy autoregressive (AR) model described in [1] is used to model and predict highspeed network traffic. This model approximates a complex nonlinear time-variant process by combining linear local autoregressive processes using a fuzzy clustering algorithm. We propose a method to estimate the traffic effective bandwidth at regular intervals, assuming the network traffic can be de...

We studied heart rate variability in rats by power scaling spectral analysis (PSSA), autoregressive modeling (AR), and detrended fluctuation analysis (DFA), assessed stability by coefficient of variation between consecutive 6-h epochs, and then compared cross-correlation among techniques. These same parameters were checked from baseline conditions through acute and chronic disease states (strep...

A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicates a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string sear...

Order selection is a general problem in autoregressive (AR) power spectrum estimation of sleep EEG. In this paper we tried to determine a common AR model order for sleep EEG. Order selection criterias are applied and the most fitting orders are selected. The effect of sampling frequency on AR modeling is investigated with experiments on EEG and sinusoidals.

This paper present a new automated detection method for cardiac arrhythmia. The detection system is implemented with integration of feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using autoregressive (AR) and multivariate autoregressive (MVAR) modeling of one-lead and two-lead electrocardiogram si...

This paper addresses model-based analysis of string instrument sounds. In particular, it reviews the application of autoregressive (AR) modeling to sound analysis/synthesis purposes. Moreover, a frequency-zooming autoregressive moving average (FZ-ARMA) modeling scheme is described. The performance of the FZ-ARMA method on modeling the modal behavior of isolated groups of resonance frequencies i...

Parametric modeling strategies are explored in conjunction with Linear Discriminant Analysis (LDA) to facilitate an Electroencephalogram (EEG) based direct-brain interface. A left/right self-paced typing exercise is analysed by employing an AutoRegressive (AR) model for feature extraction and an AutoRegressive with Exogenous input (ARX) model for combined filtering and feature extraction. Model...