Modelling 1-D signals using Hermite basis functions

Classification of ECG signals using Hermite functions and MLP neural networks

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of ...

Hermite Radial Basis Functions Implicits

The Hermite Radial Basis Functions (HRBF) Implicits reconstruct an implicit function which interpolates or approximates scattered multivariate Hermite data (i.e., unstructured points and their corresponding normals). Experiments suggest that HRBF Implicits allow the reconstruction of surfaces rich in details and behave better than previous related methods under coarse and/or nonuniform sampling...

Scattered Data Modelling Using Radial Basis Functions

Radial basis functions are traditional and powerful tools for multivariate interpolation from scattered data. This self-contained talk surveys both theoretical and practical aspects of scattered data fitting by radial basis functions. To this end, basic features of the radial basis function interpolation scheme are first reviewed, such as well-posedness, numerical stability and approximation or...

classification of ecg signals using hermite functions and mlp neural networks

classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. this paper proposes a three module system for classification of electrocardiogram (ecg) beats. these modules are: denoising module, feature extraction module and a classification module. in the first module the stationary wavelet transform (swf) is used for noise reduction of ...

Modelling with Orthonormal Basis Functions