NDLI: Blind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer’s Disease

Content Provider	IEEE Xplore Digital Library
Author	Vialatte, F. Cichocki, A. Dreyfus, G. Musha, T. Rutkowski, T.M. Gervais, R.
Copyright Year	2005
Description	Author affiliation: Lab. d'Electronique, ParisTech, Paris (Vialatte, F.)
Abstract	The early detection of Alzheimer's disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using only electroencephalographic (EEG) recordings for patients with mild cognitive impairment (MCI) without any clinical symptoms of the disease who later developed AD. In our method, first a blind source separation algorithm is applied to extract the most significant spatiotemporal uncorrelated components; afterward these components are wavelet transformed; subsequently the wavelets or more generally time frequency representation (TFR) is approximated with sparse bump modeling approach. Finally, reliable and discriminant features are selected and reduced with orthogonal forward regression and the random probe methods. The proposed features were finally fed to a simple neural network classifier. The presented method leads to a substantially improved performance (93% correctly classified - improved sensitivity and specificity) over classification results previously published on the same set of data. We hope that the new computational and machine learning tools provide some new insights in a wide range of clinical settings, both diagnostic and predictive
Starting Page	27
Ending Page	32
File Size	266637
Page Count	6
File Format	PDF
ISBN	0780395174
DOI	10.1109/MLSP.2005.1532869
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blind source separation Brain modeling Time frequency analysis Electroencephalography Alzheimer's disease Spatiotemporal phenomena Probes Neural networks Sensitivity and specificity Machine learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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