| Title |
Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features
|
|---|---|
| Published in |
Journal of Neural Transmission, February 2017
|
| DOI | 10.1007/s00702-017-1699-6 |
| Pubmed ID | |
| Authors |
Heinrich Garn, Carmina Coronel, Markus Waser, Georg Caravias, Gerhard Ransmayr |
| Abstract |
The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer's disease (AD) from Parkinson's disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects. |
Mendeley readers
The data shown below were compiled from readership statistics for 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 135 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 18% |
| Student > Ph. D. Student | 17 | 13% |
| Student > Master | 12 | 9% |
| Student > Bachelor | 12 | 9% |
| Other | 8 | 6% |
| Other | 25 | 19% |
| Unknown | 37 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 28 | 21% |
| Medicine and Dentistry | 20 | 15% |
| Psychology | 15 | 11% |
| Computer Science | 8 | 6% |
| Engineering | 5 | 4% |
| Other | 12 | 9% |
| Unknown | 47 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 March 2017.
All research outputs
#4,209,584
of 22,958,253 outputs
Outputs from Journal of Neural Transmission
#334
of 1,779 outputs
Outputs of similar age
#76,336
of 312,053 outputs
Outputs of similar age from Journal of Neural Transmission
#13
of 35 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,779 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 76% of its peers.
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We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.