Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings

Morabito, Francesco Carlo; Campolo, Maurizio; Ieracitano, Cosimo; Ebadi, M. J.; Bonanno, Lilla; Bramanti, Alessia; Desalvo, Simona; Mammone, Nadia; Bramantı, Placido

doi:10.1109/rtsi.2016.7740576

Cited by 124 publications

(74 citation statements)

References 14 publications

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“…Fourteen different algorithms were employed in [115][116][117][118][119][120][121][122][123][124][125][126]. The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size.…”

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

“…The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size. Three applications, prediction of mild cognitive impairment patients for conversion to Alzheimer's disease [119,120], prediction of mild cognitive impairment [122][123][124] and identification of genes related to Alzheimer's disease [125,126] are required to have further improvement3.4. Tuberculosis Tuberculosis (TB) was a lethal infectious disease caused by bacteria mycobacterium tuberculosis that usually attacks the lung.…”

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Section: Introductionmentioning

confidence: 99%

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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…The accuracy value of 86.8% was achieved. Francesco Carlo Morabito et al, [6]discussed the use of different biomarkers yielding promising accurate outcome and various image modalities giving a different view of the function of brain images for diagnosing AD/MCI using CNN approach (119 dementia patients: 63 AD subjects, 56 MCI subjects). For example, EEG signals classify EEG pattern of AD from the prodromal version of dementia.…”

Section: Related Workmentioning

confidence: 99%

Multi-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients

Sambath¹,

Nandhini²

2017

IJET

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“…EEG recordings are proved to be sensitive to the diseases. Francesco Carlo Morabito et al (2016) [18] proposed with EEG recordings that Deep Learning on Convolutional Neural Networks (CNN) is used to generate features that can classify AD from MCI and from HC giving an average of 80% correct classification by using a Multi layered Feedforward Perceptron (MLP).…”

Section: B Eegmentioning

confidence: 99%

Diagnosing alzheimer’s disease and mild cognitive impairment with modalities: a survey

2017

IJLTET

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Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings

Cited by 124 publications

References 14 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Multi-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients

Diagnosing alzheimer’s disease and mild cognitive impairment with modalities: a survey

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