|
ИСТИНА |
Войти в систему Регистрация |
ФНКЦ РР |
||
Early detection of Alzheimer’s disease by blind source separation, time frequency representation, and bump modeling of EEG signalsстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Дата последнего поиска статьи во внешних источниках: 5 июня 2019 г.
- Авторы: Vialatte F., Cichocki A., Dreyfus G., Musha T., Shishkin S.L., Gervais R.
- Сборник: Artificial Neural Networks: Biological Inspirations – ICANN 2005
- Серия: Lecture Notes in Computer Science
- Том: 3696
- Год издания: 2005
- Место издания: Springer Warsaw
- Первая страница: 683
- Последняя страница: 692
- DOI: 10.1007/11550822_106
- Аннотация: The early detection Alzheimer’s disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using electroencephalographic (EEG) recordings: first a blind source separation algorithm is applied to extract the most significant spatio-temporal components; these components are subsequently wavelet transformed; the resulting time-frequency representation is approximated by sparse "bump modeling"; finally, reliable and discriminant features are selected by orthogonal forward regression and the random probe method. These features are fed to a simple neural network classifier. The method was applied to EEG recorded in patients with Mild Cognitive Impairment (MCI) who later developed AD, and in age-matched controls. This method leads to a substantially improved performance (93% correctly classified, with improved sensitivity and specificity) over classification results previously published on the same set of data. The method is expected to be applicable to a wide variety of EEG classification problems. © Springer-Verlag Berlin Heidelberg 2005.
- Добавил в систему: Шишкин Сергей Львович