Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data

Munteanu, Cristian R.; Fernández-Lozano, Carlos; Mato‐Abad, Virginia; Fernández, Salvador Pita; Álvarez‐Linera, Juan; Hernández-Tamames, Juan Antonio

doi:10.1016/j.eswa.2015.03.011

Cited by 37 publications

(16 citation statements)

References 104 publications

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“…Most of the computer science research related to dementia has focused on technologies to support the early and effective diagnosis of the condition using, for example brain images [26] and magnetic resonance spectroscopy data [17]. To design such technologies, researchers such as [23] have reported the elicitation of new causal models of dementia diagnosis with domain experts.…”

Section: Digital Technologies To Support People Living With Dementiamentioning

confidence: 99%

Using a Modelling Language to Describe the Quality of Life Goals of People Living with Dementia

Lockerbie

Maiden

2019

Advanced Information Systems Engineering

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Although now well established, our information systems engineering theories and methods are applied only rarely in disciplines beyond systems development. This paper reports the application of the i* goal modelling language to describe the types of and relationships between quality of life goals of people living with dementia. Published social care frameworks to manage and improve the lives of people with dementia were reviewed to synthesize, for the first time, a comprehensive conceptual model of the types of goals of people living with dementia. Although the quality of life goal model was developed in order to construct automated reasoning capabilities in a new digital toolset that people with dementia can use for life planning, the multi-stage modelling exercise provided valuable insights into quality of life and dementia care practices of both researchers and experienced practitioners in the field.

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Section: Digital Technologies To Support People Living With Dementiamentioning

confidence: 99%

Using a Modelling Language to Describe the Quality of Life Goals of People Living with Dementia

Lockerbie

Maiden

2019

Advanced Information Systems Engineering

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“…Additionally, they have been used for Parkinson's disease diagnosis [24] or for epileptic seizure detection using EEG signals [25,26]. ANNs have also been successfully used in neurodegenerative diseases for AD prediction [27] or MCI and AD diagnosis using neuropsychological variables [18] or magnetic resonance neuroimaging biomarkers [28,29].…”

Section: Etiologymentioning

confidence: 99%

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

et al. 2018

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Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.

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“…Their experiment results obtained accuracy of 91.7 %. Munteanu et al (2015) employed Proton Magnetic Resonance Spectroscopy (MRS) data, with the aim of detecting MCI and AD. They used a single-layer perceptron with only two spectroscopic voxel volumes obtained in the left hippocampus, with an AUROC value of 0.866.…”

Section: State-of-the-artmentioning

confidence: 99%

Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

et al. 2015

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An computer-aided diagnosis system of pathological brain detection (PBD) is important for help physicians interpret and analyze medical images. We proposed a novel automatic PBD to distinguish pathological brains from healthy brains in magnetic resonance imaging scanning in this paper. The proposed method simplified the PBD problem to a binary classification task. We extracted the wavelet packet Tsallis entropy (WPTE) from each brain image. The WPTE is the Tsallis entropy of the coefficients of the discrete wavelet packet transform. The, the features were submitted to the fuzzy support vector machine (FSVM). We tested the proposed diagnosis method on 3 benchmark datasets with different sizes. A ten runs of K-fold stratified cross validation was carried out. The results demonstrated that the proposed WPTE + FSVM method excelled 17 state-of-the-art methods w.r.t. classification accuracy. The WPTE is superior to discrete wavelet transform. The Tsallis entropy performs better than Shannon entropy. The FSVM excels standard SVM. In closing, the proposed method “WPTE + FSVM” is effective in PBD.

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Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data

Cited by 37 publications

References 104 publications

Using a Modelling Language to Describe the Quality of Life Goals of People Living with Dementia

Using a Modelling Language to Describe the Quality of Life Goals of People Living with Dementia

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

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