Early Detection of Alzheimer’s Disease by Blind Source Separation and Bump Modeling of EEG Signals

The early detection Alzheimer’s disease is an important challenge. Using blind source separation, wavelet time-frequency transforms and “bump modeling” of electro-encephalographic (EEG) recordings, a set of features describing the recordings of mildly impaired patients and of controls subject is built. Feature selection by the random probe method leads to the selection of a few reliable features, which are fed to a neural network classifier. This leads to a sizeable performance improvement over detection results previously published on the same data.