Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease

Poza, Jesús; Hornero, Roberto; Abásolo, Daniel; Fernández, Alberto; García, María

doi:10.1016/j.medengphy.2006.11.006

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Volume 29, Issue 10 , Pages 1073-1083, December 2007

Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease

Jesús Poza
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain
- Corresponding author. Tel.: +34 983 42300x5569; fax: +34 983 423667.
Roberto Hornero
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain
Daniel Abásolo
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain
Alberto Fernández
- Centro de Magnetoencefalografía Dr. Pérez-Modrego, Facultad de Medicina, University Complutense of Madrid, Spain
María García
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain

Received 22 June 2006; received in revised form 14 November 2006; accepted 22 November 2006. published online 04 January 2007.

Abstract

In this study, we explored the ability of several spectral based measures to summarize the information of the power spectral density (PSD) function from spontaneous magnetoencephalographic (MEG) activity in Alzheimer's disease (AD). The MEGs of 20 AD patients and 21 elderly controls were recorded with eyes closed at rest during 5min from 148 channels. Five spectral parameters were estimated from PSD: mean frequency (MF), individual alpha frequency (IAF), transition frequency (TF), 95% spectral edge frequency (SEF95) and spectral entropy (SE). To reduce the dimensionality of the problem, we applied a principal component analysis. According to our results, MF was the best discriminating index between both groups (85.00% sensitivity, 85.71% specificity) indicating a shift to the left of the power spectrum in AD. A significant MEG slowing was also observed using both IAF (p<0.001) and TF (p<0.01). The lowest classification statistics (65% sensitivity, 66.67% specificity) were obtained with SEF95. However, these results were also significant (p<0.05). This fact points out that there is a variation in the spectral content at high frequencies of AD patients and controls. Finally, a significant decrease of irregularity in the AD group was observed with SE, with results close to those obtained with MF (90.00% sensitivity, 76.19% specificity). In conclusion, a complete description of PSD can help to increase our insight into brain dysfunction in AD and to extract spectral patterns specific to the disease.