Discrete wavelet transform EEG features of Alzheimer'S disease in activated states

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

EEG analysis in the field of neurology is customarily done using frequency domain methods like fast Fourier transform. A complex biomedical signal such as EEG is best analysed using a time-frequency algorithm. Wavelet decomposition based analysis is a relatively novel area in EEG analysis and for extracting its subbands. This work aims at exploring the use of discrete wavelet transform for extracting EEG subbands in encephalopathy. The subband energies were then calculated and given as feature sets to SVM classifier for identifying cases of encephalopathy from normal healthy subjects. Out of various combinations of subband energies, energy of delta subband yielded highest performance parameters for SVM classifier with an accuracy of 90.4% in identifying encephalopathy cases.

Wavelet analysis of pulse-amplitude-modulated chlorophyll fluorescence for differentiation of plant samples

Pulse-amplitude-modulated (PAM) chlorophyll fluorescence (ChlF) from photosystem II (PSII) of plants has been routinely measured for the analysis of photosynthesis and environmental changes. PAM ChlF from PSII is non-stationary and has time-varying frequency characteristics; however, existing analysis of PAM ChlF has been limited to selected characteristic values in the time domain. Wavelet transform is recognized as an efficient tool for analyzing non-stationary signals. In this research, an attempt was made to analyze PAM ChlF through wavelet transform. Features of PAM ChlF signals were computed from wavelet decomposition to classify two tree species and to detect chilling and detachment stresses. The wavelet-based features were compared with the commonly-used maximal PSII efficiency Fv/Fm. Both the wavelet-based features and Fv/Fm could effectively classify two tree species, but the former showed superiority than the latter in detecting the stresses. Wavelet transform revealed chilling stress earlier than Fv/Fm and detected detachment stress Fv/Fm failed to show. The results show that wavelet transform is a useful technique for analysis of PAM ChlF.