scholarly journals An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer’s Disease: Classification Using Structural Features of Brain Images

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Saima Farhan ◽  
Muhammad Abuzar Fahiem ◽  
Huma Tauseef
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Features ◽  
Vital Role ◽  
Majority Voting ◽  
Disease Classification ◽  
Ensemble Of Classifiers ◽  
Brain Images ◽  
Left Hippocampus ◽  
And Gender

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer’s disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.

scholarly journals An Approach for Assisting Diagnosis of Alzheimer’s Disease Based on Multi-Model Features of Narrative Speech

2021 ◽  
Author(s):  
Liu Ning ◽  
Qingfeng Tang ◽  
Kexue Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Semantic Feature ◽  
Majority Voting ◽  
Linguistic Features ◽  
Ensemble Of Classifiers ◽  
Linguistic Feature ◽  
Feature Parameters ◽  
Visual Spatial ◽  
Lexical Richness

Abstract Background: Alzheimer’s Disease (AD) is a common dementia which affects linguistic function, memory, cognitive and visual spatial ability of the patients. More and more studies have been done to access non-invasive, accessible, cost-effective methods for the detection of AD, Speech is proved to have relationship with AD, so a time that AD can be diagnosed in a doctor’s office is coming.Methods: In our study, the ADRess dataset in 2020 was used to detect AD which was balanced in gender and age. First we extract three categories of feature parameters: acoustic feature extracted by opensmile software, bert embeddings automatically and complicated linguistic feature extraction manually. Linguistic features are based on the POS tag, lexical Richness, fluency, semantic feature. Then seven different classifiers are used for identifying AD from normal controls, including SVM, Logistic Regress, Random forest, Extra Trees, Adaboost, LightGBM and a novel ensemble approach with majority voting strategy which is applied to overcome the error caused by a base classifier. Finally ten-fold cross validation is adopted for the evaluation of our approach. In addition, individual features and their combine features are fed to six base classifiers and ensemble of classifier. Results: We get top-performing classify result on the test set with ensemble of classifiers, the best accuracy of which is 85.4%. The best performance of feature sets are linguistic features, the accuracy of which is 85.6% with LightGBM classifier, and SFS approach is used to manifest seven discriminative linguistic features. Conclusions: The statistical and experimental results illustrates the feasibility by using speech to predict AD effectively based on acoustic and linguistic feature parameters. Stronger classifier and discriminate features are vital for the final results. We emphasise the best linguistic features for predicting AD disease are based on the POS tag, lexical Richness, fluency, semantic feature. Ensemble of classifiers usually has a better performance than single classifier.

scholarly journals Correction to: Different techniques for Alzheimer’s disease classification using brain images: a study

2021 ◽  
Author(s):  
Ruhul Amin Hazarika ◽  
Ajith Abraham ◽  
Samarendra Nath Sur ◽  
Arnab Kumar Maji ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Classification ◽  
Brain Images

Different techniques for Alzheimer’s disease classification using brain images: a study

2021 ◽  
Author(s):  
Ruhul Amin Hazarika ◽  
Ajith Abraham ◽  
Samarendra Nath Sur ◽  
Arnab Kumar Maji ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Classification ◽  
Brain Images

Examination of race and gender differences in predictors of neuropsychological decline and development of Alzheimer’s disease

2021 ◽  
pp. 1-26
Author(s):  
Ellen E. H. Johnson ◽  
Claire Alexander ◽  
Grace J. Lee ◽  
Kaley Angers ◽  
Diarra Ndiaye ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gender Differences ◽  
Race And Gender ◽  
And Gender

scholarly journals Resting State Alpha Electroencephalographic Rhythms Are Differently Related to Aging in Cognitively Unimpaired Seniors and Patients with Alzheimer’s Disease and Amnesic Mild Cognitive Impairment

2021 ◽  
pp. 1-30
Author(s):  
Claudio Babiloni ◽  
Raffaele Ferri ◽  
Giuseppe Noce ◽  
Roberta Lizio ◽  
Susanna Lopez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Diagnostic Biomarkers ◽  
Eyes Closed ◽  
Topographical Effects ◽  
And Gender ◽  
International Archive

Background: In relaxed adults, staying in quiet wakefulness at eyes closed is related to the so-called resting state electroencephalographic (rsEEG) rhythms, showing the highest amplitude in posterior areas at alpha frequencies (8–13 Hz). Objective: Here we tested the hypothesis that age may affect rsEEG alpha (8–12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with mild cognitive impairment due to Alzheimer’s disease (ADMCI). Methods: Clinical and rsEEG datasets in 63 ADMCI and 60 Nold individuals (matched for demography, education, and gender) were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands, as well as fixed beta (14–30 Hz) and gamma (30–40 Hz) bands. Each group was stratified into three subgroups based on age ranges (i.e., tertiles). Results: As compared to the younger Nold subgroups, the older one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions. On the contrary, in relation to the younger ADMCI subgroups, the older one displayed a lesser reduction in those rhythms. Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions revealed by neuroimaging, and clinical and neuropsychological scores. Conclusion: The present results suggest that age may represent a deranging factor for dominant rsEEG alpha rhythms in Nold seniors, while rsEEG alpha rhythms in ADMCI patients may be more affected by the disease variants related to earlier versus later onset of the AD.

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