Diagnosis of senile dementia by wavelet analysis of brain waves

Much research regarding time-frequency analysis using wavelet transformation (WT) has been focused on analyzing wavelets (AW), that are derived from a mathematical approach. In the analysis in this study, the measured signal is adopted as the AW. In a general way, this method is applicable to inquiry of noise in industrial instrument and analysis of sounds, vibrations and biological signals. In an application of the proposed system, the correlation between brain waves of healthy elderly people and of senile dementia sufferers is analyzed. Brain waves are complex and nonstationary signals. To apply this method to time-varying signals such as brain waves, a new concept of instantaneous correlation factor, ICF, is introduced. This method uses WT by employing the measured signal as the AW. In this study, we use brain waves as the AW. As a conclusion, it is proved that a dominant feature of the correlation can be estimated by the ICF. Time-varying correlation is effective in the analysis of brain waves and will be useful in the diagnosis of senile dementia.