It combines multiple methods including wavelet and ridgelet transforms.
In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis.
Several feature extraction methods, including wavelet packets, autoregressive (AR) parameters, and discrete Fourier transform coefficients, were employed and compared to identify crucial patterns related to P-waves in time and frequency domains.
Experimental results achieved on practical clinical data show that the proposed approach is better than several traditional methods include wavelet transform (WT), ICA, digital filter and adaptive filter in ECG interference removing.
Several methods exist to fulfill this goal including wavelet and wavelet packet decomposition [7 11], sparse signal representations using overcomplete dictionaries (such as matching pursuit [12, 13] and basis pursuit [14]), Cohen's class of time-frequency distributions [15, 16], and the recently introduced high resolution time-frequency distributions [17 20].
In addition, some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, new wavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosis are also discussed.
These methods include the wavelet and wavelet-Galerkin method [6 9], the Tikhonov method [10], the mollification method [11 13], the fundamental solution method [14], the Fourier method [15], and so on.
There exist several inverse halftoning methods, including kernel estimation [1], wavelet [2], filtering [3, 4], and set theoretic approaches [5].
The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods.
In this section, we explain the preprocessing methods, including spectra re-sampling, wavelet de-noising, baseline correction, normalization, peak detection and alignment.
Three mathematic methods including average, standard deviation and wavelet transformation have been employed to analyze the records.
