Wavelet transforms improve epilepsy diagnosis

Electroencephalograms (EEGs) have long been used as a medical analysis tool for the diagnosis of brain disorders such as epilepsy. On EEG charts, epileptic seizures are characterized in the form of sharp waveform transients that are visually examined by medical experts.

Wavelet transforms improve epilepsy diagnosis

Electroencephalograms (EEGs) have long been used as a medical analysis tool for the diagnosis of brain disorders such as epilepsy. On EEG charts, epileptic seizures are characterized in the form of sharp waveform transients that are visually examined by medical experts.

To automate the charting process, researchers at the University Hospital of Valladolid (Real de Burgos, Spain) are proposing the use of wavelet transforms to improve the visual details of such EEG transients and, therefore, to make the epileptic conditions easier to diagnose accurately. "In the past, the short-time Fourier transform has been used to characterize the nonstationary EEG signals," states Robert Hornero of the university`s communications and engineering department. "But because segment length is directly related to frequency resolution, once a window has been chosen, the time-frequency resolution is fixed over the entire time-frequency plane," he says.

To overcome this problem, Hornero used several wavelet transforms in performing a multiresolution analysis of the signals. In the system developed by Hornero and his colleagues, a modified version of the Morlet waveform was applied to the data. The squared modulus of the wavelet transform or scalogram was then plotted to visualize the results.

"A version of the Morlet wavelet was chosen because you can recognize clearer differences between normal and epileptic EEGs in their scalograms. When plotting these scalograms, the benefit of using the wavelet technique is apparent. In patients with seizures, the EEG scalogram displays more regularity than in scalograms of patients without the disorder. Such data lots also convey information much more clearly than the EEG. For example, the periodicity of the EEG signal can be measured more easily, making the epileptic condition easier to detect. This process could save time, increase objectivity and uniformity, and enable quantification for research studies," says Hornero.

Better still, the results of Hornero`s work suggest that agents producing such seizures drive the brain into a stable periodic motion for certain cases of epilepsy. For more information, contact Roberto Hornero at robhor@tel.uva.es.

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