Classification Of Dementia Research Articles

New criteria to predict LATE-NC in the clinical setting: Probable/Possible LATE and LANS.

This review discusses terminology recently proposed for the classification of dementia and, more specifically, nosology related to aging-associated TDP-43 pathology: limbic-predominant age-related TDP-43 encephalopathy (LATE), and limbic-predominant amnestic neurodegenerative syndrome (LANS). While the "gold standard" for these clinical conditions is still LATE neuropathologic changes (LATE-NC), clinical criteria and biomarkers are evolving. The newly proposed clinical rubrics are discussed with emphasis on the need for terminology that acknowledges the distinctions between clinical syndrome-, molecular biomarker-, and pathologically defined disease concepts. As further progress is made on research into the specific biomarker-based detection and prediction of TDP-43 proteinopathy in the clinical setting, the definitions of "Probable" and "Possible" LATE are likely to become more useful clinically. For people interested in the pathological diagnoses or basic research related to LATE-NC, the relevant terminology remains unchanged by the newly proposed clinical criteria.

Prevalence of Hearing Loss and Hearing Aid Use Among Persons Living With Dementia in the US

Dementia and hearing loss are prevalent and represent public health priorities, but little is known about the prevalence of hearing loss among persons living with dementia at a population level. To estimate the prevalence of hearing loss and hearing aid use among a nationally representative sample of persons living with dementia. In this cross-sectional study, we analyzed data from Medicare beneficiaries who participated in round 11 of the National Health and Aging Trends Study (NHATS). The analytic sample included participants who had complete audiometric data and a dementia classification, and survey weights were applied to yield nationally representative estimates. Data were collected from June to November 2021, and analyzed from October 12, 2023, to February 27, 2024. Audiometric measures of hearing loss and self-reported hearing aid use. National prevalence estimates of possible and probable dementia and hearing loss were estimated by applying NHATS survey weights of sex and age to the population aged 71 years or older based on the US Census Bureau's 2020 American Community Survey data. The analytic sample comprised 2613 NHATS participants (74.6% aged 71-79 years and 25.4% aged ≥80 years; 52.9% female). Overall prevalence of hearing loss among participants with dementia was estimated at 79.4% (95% CI, 72.1%-85.3%). The prevalence of hearing loss increased with age, from 61.1% (95% CI, 37.7%-80.2%) among participants aged 71 to 74 years to 94.2% (95% CI, 88.8%-97.0%) among those aged 85 years or older. Among participants with hearing loss, 21.7% (95% CI, 16.2%-28.3%) reported hearing aid use. In this cross-sectional study, the findings suggest that hearing loss is highly prevalent among individuals with dementia, with a low prevalence of hearing aid use. These findings reveal a potential opportunity for intervention.

Dementia Classification Approach Based on Non-Singleton General Type-2 Fuzzy Reasoning

Dementia is the most critical neurodegenerative disease that gradually destroys memory and other cognitive functions. Therefore, early detection is essential, and to build an effective detection model, it is required to understand its type, symptoms, stages and causes, and diagnosis methodologies. This paper presents a novel approach to classify dementia based on a data set with some relevant patient features. The classification methodology employs non-singleton general type-2 fuzzy sets, non-singleton interval type-2 fuzzy sets, and non-singleton type 1 fuzzy sets. These advanced fuzzy sets are compared with traditional singleton fuzzy sets to evaluate their performance. The Takagi–Sugeno–Kang TSK inference method is used to handle fuzzy reasoning. In the process, the parameters of the membership functions (MFs) and rules are obtained using ANFIS, and non-singleton MFs are optimized with PSO. The results demonstrate that non-singleton general type-2 fuzzy sets improve classification accuracy compared to singleton fuzzy sets, demonstrating their ability to model the uncertainties inherent in the diagnosis of dementia. This improvement suggests that non-singleton fuzzy systems offer a more robust framework for developing effective diagnostic tools in the medical domain. Accurate classification of dementia is of utmost importance to improve patient care and advance medical research.

Predictors of mortality over time in geriatric patients with hip fracture.

The aim of this study was to determine the variations in effect for predictors of mortality over time and risk of in-hospital complications in geriatric patients with a hip fracture. Many studies have investigated risk factors of short-term and long-term mortality separately. In current literature, little is known about the variations in effect of risk factors over time and no comparison with the general population is made. All patients with a hip fracture aged 70 years or older admitted to our hospital between January 1, 2016, and May 1, 2018, were included in this retrospective study. Patients who had undergone total hip arthroplasty (THA) were not included. The primary outcome was mortality after 1 year. Secondary outcomes were mortality after 30 days, 90 days, 2 years, and complications. Kaplan-Meier (KM) curves for risk factors were generated to visualize survival over time. Data were compared with data extracted from the national health records. A total of 685 geriatric patients with hip fractures were included with a 1-year mortality of 27%. The adjusted odds ratios (AOR) found differed over time. Five risk factors for mortality were investigated in this study using KM curves: age, prefracture living situation, dementia, sex, and ASA classification. Over time, the variation of 5 risk factors for mortality were visualized in geriatric patients with a hip fracture: age, prefracture living situation, dementia, sex, and ASA classification. An elevated risk of mortality was discovered compared with the general population. The variation in effect observed in risk factors plays a vital role in prognosis. This insight will help guide accurate medical decision-making for a tailored treatment plan for geriatric patients with a hip fracture.

Dementia diagnosis in young adults: a machine learning and optimization approach

AbstractIndividuals who are younger and have dementia often start experiencing its symptoms before they turn 65, with cases even documented in people as young as their thirties. Researchers strive for accurate dementia diagnosis to slow or halt its progression. This paper presents a novel Enhanced Dementia Detection and Classification Model (EDCM) comprised of four modules: data acquisition, preprocessing, hyperparameter optimization, and feature extraction/classification. Notably, the model uses texture information from segmented brain images for improved feature extraction, leading to significant gains in both binary and multi-class classification. This is achieved by selecting optimal features via a Gray Wolf Optimization (GWO)-driven enhancement model. Results demonstrate substantial accuracy improvements after optimization. For instance, using an Extra Tree Classifier for "normal" cases, the model achieves 85% accuracy before optimization. However, with GWO-optimized features and hyperparameters, the accuracy jumps to 97%.

Nutritional status, dietary quality and eating disturbance issues among people with dementia in Vietnam: evidence of a cross-sectional study

BackgroundDue to cognitive impairments, people with dementia (PWD) often have difficulties in eating and drinking. This study aimed to assess the nutritional status, dietary quality and eating disturbance issues among PWD in Vietnam.MethodsWe conducted a cross-sectional study at the Vietnamese National Geriatric Hospital from April to December 2022. We used Mini-Mental State Exam (MMSE) to classify the severity levels of dementia. Mini Nutritional Assessment (MNA), 24-hour recall, eating disturbance questionnaires, and anthropometric indicators were used to evaluate the nutritional status, dietary quality, and eating disorders of study subjects.ResultsOverall, among 63 study participants, 74.6 per cent of PWD were at risk of or having malnutrition. By dementia classification according to MMSE scale, people with moderate and severe dementia accounted for 53.3 per cent of those who met the recommended energy levels, compared to 42.4 per cent of people with mild dementia and normal people. In the above two groups, around three per cent of participants reached the recommended amount of fibre. Calcium (50–70%), vitamin A (80–90%), and D (90%) were found to be the most severe deficiency forms of minerals and vitamins in both male and female participants. The majority of participants (90.5%) had at least one form of eating disorders with the most frequent issue being appetite changes (76.2%) and swallowing issues (50.8%).ConclusionsPWD in our sample frequently experienced malnutrition, a lack of essential nutrients, difficulties swallowing, changes in eating habits and appetite. It is neccesary to early screen and assess nutritional status and swallowing disturbance in PWD, and instruct their caregivers to prepare nutritious meals for them.

Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant.

To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset. Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns. The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification. Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions. This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification.

Multimodal deep learning for dementia classification using text and audio

Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early diagnosis is important to enhance the lifespan and quality of life for affected individuals. Despite its importance, diagnosing dementia is a complex process. Automated machine learning solutions involving multiple types of data have the potential to improve the process of automated dementia screening. In this study, we build deep learning models to classify dementia cases from controls using the Pitt Cookie Theft dataset from DementiaBank, a database of short participant responses to the structured task of describing a picture of a cookie theft. We fine-tune Wav2vec and Word2vec baseline models to make binary predictions of dementia from audio recordings and text transcripts, respectively. We conduct experiments with four versions of the dataset: (1) the original data, (2) the data with short sentences removed, (3) text-based augmentation of the original data, and (4) text-based augmentation of the data with short sentences removed. Our results indicate that synonym-based text data augmentation generally enhances the performance of models that incorporate the text modality. Without data augmentation, models using the text modality achieve around 60% accuracy and 70% AUROC scores, and with data augmentation, the models achieve around 80% accuracy and 90% AUROC scores. We do not observe significant improvements in performance with the addition of audio or timestamp information into the model. We include a qualitative error analysis of the sentences that are misclassified under each study condition. This study provides preliminary insights into the effects of both text-based data augmentation and multimodal deep learning for automated dementia classification.

Classification of Vascular Dementia on magnetic resonance imaging using deep learning architectures

Vascular Dementia is a severe disease that results from dead nerve cells’ accumulation in blood vessels. This affects the blood flow and impairs memory and decision-making abilities. Machine learning and deep learning have been used in detecting this disease. Nevertheless, their accuracy has been inconsistent, explaining why their utilization in diagnosing patients has led to poor performance. We developed several transfer learning architectures that improve classification accuracy and diagnosis performance in assessing vascular dementia. The process first entails the preprocessing of the dataset where a random selection ensures data representation is balanced. We used a dataset containing resting-state fMRI scans to split training, testing, and validation into 80%, 10%, and 10%. We employ different Convolutional Neural Network architectures such as VGG16, VGG19, DenseNet121, and InceptionResNetV2 to enhance classification. To, enhance these, we incorporated Rectified Linear Unit and leaky activation functions for the training phase to counteract problems associated with vanishing gradient common in deep learning tasks. Our methodology ensures effective information flow throughout different layers, which is essential for a divergent information hierarchy in medical information. As such, the specifics show that our approach achieved 84.67% in accuracy in the multi-classification, which is better than the current state-of-the-art research in the same field. Therefore, the result shows that transfer learning-based approaches are suitable when combined with strategic pre-processing and activation functions in improving the diagnosis of vascular dementia using MRI images.

Selection and Progress of drugs for treating Dementia

Dementia is a major health problem among the elderly around the world; the number of dementia patients, as well as antidementia drugs, has been increasing in recent years. In this paper, we searched the dementia treatment guidelines, domestic and foreign literature reports in the past five years, summarized the classification of dementia, the selection of current pharmacotherapies in dementia, evidence-based evaluation of guidelines and some drugs with clinical treatment prospects to provide a reference for the decision-making of dementia drug therapy.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features. The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN. The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

Performance of probable dementia classification in a European multi-country survey

Feasibility constraints limit availability of validated cognitive assessments in observational studies. Algorithm-based identification of ‘probable dementia’ is thus needed, but no algorithm developed so far has been applied in the European context. The present study sought to explore the usefulness of the Langa–Weir (LW) algorithm to detect ‘probable dementia’ while accounting for country-level variation in prevalence and potential underreporting of dementia. Data from 56 622 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE, 2017) aged 60 years and older with non-missing data were analyzed. Performance of LW was compared to a logistic regression, random forest and XGBoost classifier. Population-level ‘probable dementia’ prevalence was compared to estimates based on data from the Organisation for Economic Co-operation and Development. As such, application of the prevalence-specific LW algorithm, based on recall and limitations in instrumental activities of daily living, reduced underreporting from 61.0 (95% CI, 53.3–68.7%) to 30.4% (95% CI, 19.3–41.4%), outperforming tested machine learning algorithms. Performance in other domains of health and cognitive function was similar for participants classified ‘probable dementia’ and those self-reporting physician-diagnosis of dementia. Dementia classification algorithms can be adapted to cross-national cohort surveys such as SHARE and help reduce underreporting of dementia with a minimal predictor set.

VentRa: distinguishing frontotemporal dementia from psychiatric disorders.

The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia. We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature-feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%-70% accuracy with Naïve Bayes). This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature-feature correlations and their results can vary between cohort studies. By understanding these biases, we can reduce discrepancies in feature ranking and identify a minimal set of features needed for accurate classification of dementia.

The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes

BackgroundThe use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach.MethodsStudy participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained.ResultsThree hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637).ConclusionsBrain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.

Physical Activity Intensity and Risk of Dementia

IntroductionRegular participation in aerobic physical activity is associated with a reduced risk of dementia. It is currently unclear whether this association is due to the total volume or intensity of physical activity. MethodsThis prospective cohort study analyzed 386,486 adults from the UK Biobank who were free of dementia and self-reported >0 minutes of moderate-to-vigorous intensity physical activity (MVPA) at baseline (2007-2010). Participants were categorized as performing 0%, >0% to 30%, or >30% of their total MVPA in vigorous activity (VPA). Cox proportional hazards regression models were used to examine the associations between categories of VPA and incident dementia while adjusting for sociodemographic and lifestyle factors including total MVPA. Analyses were performed in 2022. ResultsOver an average follow-up of 12.0 (1.7) years, there were 5,177 (1.3%) cases of dementia. Compared to the group reporting 0% VPA, the hazard ratios (95% confidence intervals) of dementia for the groups reporting >0% to 30% and >30% VPA were 0.73 (0.68-0.78) and 0.81 (0.75-0.87), respectively, in the fully adjusted model. In a joint analysis, reporting some VPA was associated with a reduced risk of dementia regardless of meeting the aerobic physical activity guidelines (HR=0.78 [0.72-0.85]) or not (HR=0.76 [0.60-0.98]), while meeting the aerobic physical activity guidelines alone without VPA was not associated with incident dementia (HR=0.98 [0.90-1.07]), compared to the group that did not meet the guidelines and reported no VPA. ConclusionsThese results suggest that engaging in VPA as part of MVPA is associated with a lower risk of dementia.

Useful blunders: Can automated speech recognition errors improve downstream dementia classification?

Objectives:We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the “Cookie Theft” picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer’s disease (AD). Methods:We conducted experiments using various ASR models, refining their transcripts with post-editing techniques. Both these imperfect ASR transcripts and manually transcribed ones were used as inputs for the downstream dementia classification. We conducted comprehensive error analysis to compare model performance and assess ASR-generated transcript effectiveness in dementia classification. Results:Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the “Cookie Theft” task. These ASR-based models surpassed the previous state-of-the-art approach, indicating that ASR errors may contain valuable cues related to dementia. The synergy between ASR and classification models improved overall accuracy in dementia classification. Conclusion:Imperfect ASR transcripts effectively capture linguistic anomalies linked to dementia, improving accuracy in classification tasks. This synergy between ASR and classification models underscores ASR’s potential as a valuable tool in assessing cognitive impairment and related clinical applications.

Classification of dementia from spoken speech using feature selection and the bag of acoustic words model

&lt;p&gt;Memory disorders and dementia are a central factor in the decline of functioning and daily activities in older individuals. The workload related to standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken speech. This study presented a bag of acoustic words approach for distinguishing dementia patients from control individuals based on audio speech recordings. In this approach, each individual's speech was segmented into voiced periods, and these segments were characterized by acoustic features using the open-source openSMILE library. Word histogram representations were formed from the characterized speech segments of each speaker, which were used for classifying subjects. The formation of word histograms involved a clustering phase where feature vectors were quantized. It is well-known that partitional clustering involves instability in clustering results due to the selection of starting points, which can cause variability in classification outcomes. This study aimed to address instability by utilizing robust K-spatial-medians clustering, efficient K-means$ ++ $ clustering initialization, and selecting the smallest clustering error from repeated clusterings. Additionally, the study employed feature selection based on the Wilcoxon signed-rank test to achieve computational efficiency in the methods. The results showed that it is possible to achieve a consistent 75% classification accuracy using only twenty-five features, both with the external ADReSS 2020 test data and through leave-one-subject-out cross-validation of the entire dataset. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.&lt;/p&gt;

Analysis of EEG signals with the use of wavelet transform for accurate classification of Alzheimer Disease, Frontotemporal Dementia and healthy subjects using Machine Learning Models

Dementia is a brain disorder, if not prevented; takes the form of various types of diseases that have no cure yet. Accurate classification of multiple types of dementia diseases is required to provide proper medication to the patient so that growth of that disease can be delayed. This study analyzes EEG signal for the classification of multiple dementia diseases such as Alzheimer’s disease (AD), Fronto-temporal dementia (FTD) and control normal (CN) subjects using machine learning (ML) algorithms. Each of the 19 channels of EEG dataset is analyzed separately in this work to perform the classification. Combination of parameters like Hjorth Activity, Mobility and Complexity along with kurtosis value of the data has been extracted in time-frequency domain for each EEG frequency band (Delta, Theta, Alpha, Beta and Gamma) is applied to the machine learning algorithms. This research is focused on classification of multiple dementia classes (ADvsFTD) as well as three-way (ADvsFTDvsCN) classification. This research is validated using public EEG dataset with 23 participants of each category. Best classification result is achieved using random forest classifier and leave-one-subject-out (LOSO) cross validation method. The three-way classification i.e., ADvsCNvsFTD achieved best accuracy of 75.29%, whereas binary classifications i.e. ADvsCN, ADvsFTD and CNvsFTD achieved best accuracy of 88.90%, 88.44% and 84.10% respectively. The proposed framework shows better results than existing work on dementia classification using machine learning. The results obtained from proposed framework showed that combination of EEG frequency band features can be utilized for the classification of multiple dementia diseases with greater accuracy.

Empirical Assessment of Transfer Learning Strategies for Dementia Classification Using MRI Images

Dementia is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is very crucial for effective management. Magnetic resonance imaging (MRI) offers a noninvasive means to assess structural brain changes associated with dementia. In this study, we propose an empirical evaluation for binary classification of dementia using MRI images, utilizing transfer learning techniques applied to a diverse array of pretrained deep learning models. This paper presents a systematic comparison of the performance of various transfer learning approaches, including feature extraction and fine-tuning, across a spectrum of popular pretrained models, such as visual geometry group (VGG), Inception, ResNet, EfficientNet, and DenseNet. This paper also investigates the effects of the transfer learning approach on classification accuracy. Experimental results show that transfer learning is effective in improving classification performance, and they are validated on a large dataset of MRI scans from subjects with and without dementia. Furthermore, the relative benefits and drawbacks of various transfer learning techniques and pretrained models for dementia classification are revealed by the comparative analysis. The results of this investigation enhance the development of automated diagnostic instruments for dementia, thereby promoting prompt intervention and enhanced patient results.