Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.
We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order t...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 9; pp. 843 - 856 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2015
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109466525&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109466525 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2015 vid: 53 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109466525 109466525 NLM25863694 109466525 10.1007/s11517-015-1298-3 NLM25863694 109466525 ppf: 843 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Exploration of EEG features of Alzheimer's disease using continuous wavelet transform. aug: au: Ghorbanian, Parham Devilbiss, David Hess, Terry Bernstein, Allan Simon, Adam Ashrafiuon, Hashem Devilbiss, David M Simon, Adam J affil: Center for Nonlinear Dynamics and Control, Villanova University, Villanova 19085 USA sug: subj: Alzheimer's Disease Diagnosis Signal Processing, Computer Assisted Electroencephalography Aged Aged, 80 and Over Mathematics Decision Trees Brain Pathology Reproducibility of Results Female Middle Age Male Human Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|