Extraction of Diagnostic Information on Brain Diseases by Analyzing Wavelet Spectra of Biomedical Signals

New approaches to the analysis of Morlet wavelet spectra of electroencephalograms, electromyograms, and accelerometer signals are proposed. The proposed approaches are based on the analysis of time-frequency distributions of local extrema and ridges of wavelet spectrograms. The paper describes the results of application of the proposed techniques for the diagnosis of the early stage of Parkinson's disease and essential tremor, monitoring of postoperative patients with epilepsy, and assessment of inter-channel phase coupling of EEG during cognitive tests of patients after traumatic brain injury.

Automated summary

New approaches based on the analysis of local extrema and ridges of wavelet spectrograms are proposed for the analysis of Morlet wavelet spectra of EEG, EMG, and accelerometer signals. The techniques have been applied successfully in diagnosing early stage Parkinson's disease and essential tremor, monitoring postoperative epilepsy patients, and assessing inter-channel phase coupling of EEG during cognitive tests of patients after traumatic brain injury.