| Title |
Analysis of long range dependence in the EEG signals of Alzheimer patients
|
|---|---|
| Published in |
Cognitive Neurodynamics, January 2018
|
| DOI | 10.1007/s11571-017-9467-8 |
| Pubmed ID | |
| Authors |
T. Nimmy John, Subha D. Puthankattil, Ramshekhar Menon |
| Abstract |
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 50% |
| Netherlands | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 58 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 17% |
| Student > Doctoral Student | 8 | 14% |
| Researcher | 7 | 12% |
| Student > Bachelor | 6 | 10% |
| Student > Master | 5 | 9% |
| Other | 8 | 14% |
| Unknown | 14 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 8 | 14% |
| Neuroscience | 8 | 14% |
| Medicine and Dentistry | 6 | 10% |
| Computer Science | 6 | 10% |
| Psychology | 5 | 9% |
| Other | 7 | 12% |
| Unknown | 18 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 30 March 2018.
All research outputs
#2,910,093
of 23,015,156 outputs
Outputs from Cognitive Neurodynamics
#15
of 321 outputs
Outputs of similar age
#67,963
of 441,866 outputs
Outputs of similar age from Cognitive Neurodynamics
#2
of 16 outputs
Altmetric has tracked 23,015,156 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 321 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 95% of its peers.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.