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Combining EEG signal processing with supervised methods for Alzheimer’s patients classification

Overview of attention for article published in BMC Medical Informatics and Decision Making, May 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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1 news outlet
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1 patent

Citations

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103 Dimensions

Readers on

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168 Mendeley
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Title
Combining EEG signal processing with supervised methods for Alzheimer’s patients classification
Published in
BMC Medical Informatics and Decision Making, May 2018
DOI 10.1186/s12911-018-0613-y
Pubmed ID
Authors

Giulia Fiscon, Emanuel Weitschek, Alessio Cialini, Giovanni Felici, Paola Bertolazzi, Simona De Salvo, Alessia Bramanti, Placido Bramanti, Maria Cristina De Cola

Abstract

Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms. In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods. By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively. Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 168 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 17%
Student > Master 26 15%
Student > Bachelor 15 9%
Researcher 12 7%
Student > Doctoral Student 11 7%
Other 33 20%
Unknown 42 25%
Readers by discipline Count As %
Engineering 44 26%
Computer Science 31 18%
Medicine and Dentistry 12 7%
Neuroscience 9 5%
Psychology 5 3%
Other 14 8%
Unknown 53 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 16 September 2021.
All research outputs
#2,987,232
of 23,088,369 outputs
Outputs from BMC Medical Informatics and Decision Making
#241
of 2,013 outputs
Outputs of similar age
#63,446
of 331,179 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#5
of 17 outputs
Altmetric has tracked 23,088,369 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,013 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,179 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 17 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 70% of its contemporaries.