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

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.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/862307 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 30

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.

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Identifying Mild Cognitive Impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features

Computer Speech & Language ◽

10.1016/j.csl.2018.07.007 ◽

2019 ◽

Vol 53 ◽

pp. 181-197 ◽

Cited By ~ 21

Author(s):

Gábor Gosztolya ◽

Veronika Vincze ◽

László Tóth ◽

Magdolna Pákáski ◽

János Kálmán ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Spontaneous Speech ◽

Linguistic Features

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Role of Cofilin in Alzheimer’s Disease

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.584898 ◽

2020 ◽

Vol 8 ◽

Author(s):

Qiang Wang ◽

Wei Yuan ◽

Xiaohang Yang ◽

Yuan Wang ◽

Yongfeng Li ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyloid Plaques ◽

Executive Dysfunction ◽

Actin Binding ◽

Synaptic Dysfunction ◽

Hyperphosphorylated Tau ◽

Hirano Bodies ◽

Visual Spatial ◽

Β Amyloid

Alzheimer’s disease (AD) is a degenerative neurological disease and has an inconspicuous onset and progressive development. Clinically, it is characterized by severe dementia manifestations, including memory impairment, aphasia, apraxia, loss of recognition, impairment of visual-spatial skills, executive dysfunction, and changes in personality and behavior. Its etiology is unknown to date. However, several cellular biological signatures of AD have been identified such as synaptic dysfunction, β-amyloid plaques, hyperphosphorylated tau, cofilin-actin rods, and Hirano bodies which are related to the actin cytoskeleton. Cofilin is one of the most affluent and common actin-binding proteins and plays a role in cell motility, migration, shape, and metabolism. They also play an important role in severing actin filament, nucleating, depolymerizing, and bundling activities. In this review, we summarize the structure of cofilins and their functional and regulating roles, focusing on the synaptic dysfunction, β-amyloid plaques, hyperphosphorylated tau, cofilin-actin rods, and Hirano bodies of AD.

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Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2006.08.012 ◽

2007 ◽

Vol 37 (4) ◽

pp. 542-558 ◽

Cited By ~ 49

Author(s):

Robi Polikar ◽

Apostolos Topalis ◽

Deborah Green ◽

John Kounios ◽

Christopher M. Clark

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Diagnosis ◽

Wavelet Analysis ◽

Ensemble Of Classifiers ◽

Spectral Bands ◽

Multiresolution Wavelet Analysis ◽

Multiresolution Wavelet

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Linguistic Parameters of Spontaneous Speech for identifying Mild Cognitive Impairment and Alzheimer’s Disease

Computational Linguistics ◽

10.1162/coli_a_00428 ◽

2021 ◽

pp. 1-34

Author(s):

Veronika Vincze ◽

Martina Katalin Szabó ◽

Ildikó Hoffmann ◽

László Tóth ◽

Magdolna Pákáski ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Spontaneous Speech ◽

Healthy Controls ◽

Demographic Information ◽

Linguistic Features ◽

Significance Level ◽

Speech Transcripts

Abstract In this paper, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer’s Disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients and mAD patients, we will carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the paper, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we will analyze the efficacy of each feature group separately. Our model which uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68–70%, 2-class accuracy values: 77.3–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.

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Using linguistic features longitudinally to predict clinical scores for Alzheimer's disease and related dementias

10.18653/v1/w15-5123 ◽

2015 ◽

Cited By ~ 6

Author(s):

Maria Yancheva ◽

Kathleen Fraser ◽

Frank Rudzicz

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Linguistic Features ◽

Clinical Scores

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C-15 Serotonergic System Activity and BPSD in Alzheimer’s Disease Pathogenesis: A Systematic Review

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acz034.177 ◽

2019 ◽

Vol 34 (6) ◽

pp. 1044-1044 ◽

Cited By ~ 1

Author(s):

J Lennon ◽

B Sytsma ◽

A Mohit ◽

S Patel

Keyword(s):

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Serotonergic System ◽

Data Selection ◽

Pharmacological Intervention ◽

Ongoing Research ◽

Recurrent Seizure ◽

Visual Spatial ◽

The Impact

Abstract Objective The 5-hydroxytryptamine (5-HT) system is heavily implicated in behavioral and psychological symptoms of dementia (BPSD), with substantial bases for ongoing research in Alzheimer’s disease (AD). This system is directly tied to the hypothalamic-pituitary-adrenal axis. This systematic review aims to accomplish the following objectives: 1) introduce noteworthy BPSD found in AD; 2) synthesize research on 5-HT and BPSD in AD; 3) discuss neuropsychological sequelae of serotonergic dysregulation in AD; and, 4) report future research directions. Data Selection Data Selection: We conducted a literature search of the Medline, PubMed, psychINFO, and Google Scholar databases using the following keywords: Alzheimer’s*, seroton* (serotonin, serotonergic), 5-HT* (5-HTR, 5-HTT*), neuropsychology, behavior*, cogniti*. From the list of studies obtained through this search, we then employed the following inclusion criteria: 1) individuals in study had a formal diagnosis of probable or suspected AD; 2) individuals in study had not previously experienced head trauma, recurrent seizure, or other neurological insult; 3) sample did not include participants with comorbid personality disorders. Data Synthesis Findings suggest that serotonin’s receptors (5-HTRs), transporter (5-HTT), metabolite (5-HTP), and transporter-linked polymorphic region (5-HTTLPR) are linked to depression, anxiety, hyperactivity/impulsivity, aggression, and apathy in AD. Further, 5-HT and resultant BPSD are implicated in numerous cognitive functions including but not limited to decision-making, visual-spatial deficits, attention and vigilance, episodic memory, global cognitive function. Conclusions Substantial evidence exists implicating the serotonergic system in BPSD in AD. By understanding the impact of 5-HT on disease trajectory, neurocognitive functioning, and neuropsychological test performance, clinicians can ensure that appropriate recommendations are made for psychosocial and pharmacological intervention.

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Criteria for the diagnosis of Alzheimer's disease: Recommendations of the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology

Dementia & Neuropsychologia ◽

10.1590/s1980-57642011dn05030002 ◽

2011 ◽

Vol 5 (3) ◽

pp. 146-152 ◽

Cited By ~ 16

Author(s):

Norberto Anízio Ferreira Frota ◽

Ricardo Nitrini ◽

Benito Pereira Damasceno ◽

Orestes V. Forlenza ◽

Elza Dias-Tosta ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Phase ◽

Visual Spatial ◽

Cerebrospinal Fluid Biomarkers ◽

Diagnosis Of Dementia ◽

Research Criteria ◽

Initial Forms ◽

New Criteria ◽

Cognitive Neurology

Abstract This consensus prepared by the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology is aimed at recommending new criteria for the diagnosis of dementia and Alzheimer's disease (AD) in Brazil. A revision was performed of the proposals of clinical and of research criteria suggested by other institutions and international consensuses. The new proposal for the diagnosis of dementia does not necessarily require memory impairment if the cognitive or behavioral compromise affects at least two of the following domains: memory, executive function, speech, visual-spatial ability and change in personality. For the purpose of diagnosis, AD is divided into three phases: dementia, mild cognitive impairment and pre-clinical phase, where the latter only applies to clinical research. In the dementia picture, other initial forms were accepted which do not involve amnesia and require a neuroimaging examination. Cerebrospinal fluid biomarkers are recommended for study, but can be utilized as optional instruments, when deemed appropriate by the clinician.

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