Dementia Recognition Research Articles

D‐Unet: A symmetric architecture of convolutional neural network with two auxiliary outputs for dementia recognition

AbstractDementia‐associated disorders cause damage to the brains of patients and bring huge burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to patients on account of low cost, non‐invasion, and objective. Event‐related potential (ERP) is a component of EEG that has huge potential to evaluate the cognitive function of the brain. In this study, we recorded the ERP from patients with dementia and healthy people, then proposed an ERP‐based deep learning method to realize dementia recognition via the model Dementia‐Unet (D‐Unet). To improve the performance of the model, on the base of the decoder and the primary classifier, we added new structures including a symmetric decoder and two auxiliary outputs. One of the auxiliary outputs was input reconstruction, and the other one was aimed at working like the primary classifier with the same task. The results of the experiment of four‐fold cross‐validation demonstrated the two auxiliary outputs improved the performance of the model effectively. When compared with some other machine learning methods and deep learning methods, our model obtained the best performance with an accuracy of 0.815, a precision of 0.829, a recall of 0.797, and an f1‐score of 0.812. Besides, we put up a complex training strategy with all outputs involved, but a simple testing strategy with only a primary classifier working to keep high performance but cut down the complexity burden during testing.

An Overview of the ADReSS-M Signal Processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition Through Spontaneous Speech.

The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.

Machine Learning-Based Prediction Models for Cognitive Decline Progression: A Comparative Study in Multilingual Settings Using Speech Analysis.

Mild cognitive impairment (MCI) is a condition commonly associated with dementia. Therefore, early prediction of progression from MCI to dementia is essential for preventing or alleviating cognitive decline. Given that dementia affects cognitive functions like language and speech, detecting disease progression through speech analysis can provide a cost-effective solution for patients and caregivers. In our study, we examined spontaneous speech (SS) and written Mini Mental Status Examination (MMSE) scores from a 60-patient dataset obtained from the Mugla University Dementia Outpatient Clinic (MUDC) and a 153-patient dataset from the Alzheimer's Dementia Recognition through Spontaneous Speech (ADRess) challenge. Our study, for the first time, analyzed the impact of audio features extracted from SS in distinguishing between different degrees of cognitive impairment using both an Indo-European language and a Turkic language, which exhibit distinct word order, agglutination, noun cases, and grammatical markers. When each machine learning model was tested on its respective trained language, we attained a 95% accuracy using the random forest classifier on the ADRess dataset and a 94% accuracy on the MUDC dataset employing the multilayer perceptron (MLP) neural network algorithm. In our second experiment, we evaluated the effectiveness of each language-specific machine learning model on the dataset of the other language. We achieved accuracies of 72% for English and 76% for Turkish, respectively. These findings underscore the cross-language potential of audio features for automated tracking of cognitive impairment progression in MCI patients, offering a convenient and cost-effective option for clinicians or patients.

A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition.

Human activity recognition (HAR) through gait analysis is a very promising research area for early detection of neurodegenerative diseases because gait abnormalities are typical symptoms of some neurodegenerative diseases, such as early dementia. While working with such biometric data, the performance parameters must be considered along with privacy and security issues. In other words, such biometric data should be processed under specific security and privacy requirements. This work proposes an innovative hybrid protection scheme combining a partially homomorphic encryption scheme and a cancelable biometric technique based on random projection to protect gait features, ensuring patient privacy according to ISO/IEC 24745. The proposed hybrid protection scheme has been implemented along a long short-term memory (LSTM) neural network to realize a secure early dementia diagnosis system. The proposed protection scheme is scalable and implementable with any type of neural network because it is independent of the network's architecture. The conducted experiments demonstrate that the proposed protection scheme enables a high trade-off between safety and performance. The accuracy degradation is at most 1.20% compared with the early dementia recognition system without the protection scheme. Moreover, security and computational analyses of the proposed scheme have been conducted and reported.

Context is not key: Detecting Alzheimer’s disease with both classical and transformer-based neural language models

Natural language processing (NLP) has exhibited potential in detecting Alzheimer’s disease (AD) and related dementias, particularly due to the impact of AD on spontaneous speech. Recent research has emphasized the significance of context-based models, such as Bidirectional Encoder Representations from Transformers (BERT). However, these models often come at the expense of increased complexity and computational requirements, which are not always accessible. In light of these considerations, we propose a straightforward and efficient word2vec-based model for AD detection, and evaluate it on the Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS) challenge dataset. Additionally, we explore the efficacy of fusing our model with classic linguistic features and compare this to other contextual models by fine-tuning BERT-based and Generative Pre-training Transformer (GPT) sequence classification models. We find that simpler models achieve a remarkable accuracy of 92% in classifying AD cases, along with a root mean square error of 4.21 in estimating Mini-Mental Status Examination (MMSE) scores. Notably, our models outperform all state-of-the-art models in the literature for classifying AD cases and estimating MMSE scores, including contextual language models.

Strengthening home‐based dementia care services in Lithuania – accessibility, assessment, and delivery

AbstractBackgroundIncreased diagnostic rates of dementia in Lithuania were an essential factor that led to changes in the legislative framework. At the same time, there is still capacity for change in dementia diagnostic tools, as multiple methods and techniques are currently used. Therefore the development of a single diagnostic process is critical. In addition, family physicians are anticipated to play a more significant part in the diagnostic process due to the increasing number of people living with dementia (PLWD), as family physicians are directly linked to homecare teams.AimsThis study aimed to identify ways homecare teams could deliver customized care and the barriers that impede access to these services. Identifying these subjects could improve understanding of how legislation addresses the accessibility and quality of dementia‐related services for those in need.MethodsRevision of legal acts and related regulations has taken part, which included analysis of the content of services provided at home, amount and length of services, accessibility, and waiting times. Revision of requirements for professionals working in community settings and their postgraduate education programs were scrutinized at a large scale during this study. A home care assessment tool was also included in the research to determine whether people with unrecognized dementia would be entitled to receive care.ResultsIt was revealed that the family physician role is sparse in dementia recognition, and quite frankly, patients with dementia symptoms are sent for psychiatric consultation to confirm the diagnosis. As a result, it leads to a bureaucratic and long‐lasting process, and the start of care is postponed. The study also identified that professionals working at homecare teams do not receive specialized training to address PLWD needs. In addition, nurses are not taught how to recognize primary dementia symptoms and report them. Moreover, legal acts do not oblige healthcare providers to promote and maintain the well‐being of those in need and their caregivers. Emotional well‐being, personal dignity, and control over daily life are also not addressed. Although an assessment tool is structured, people with primary cognitive symptoms would be able to receive home‐based care.

Subjective Cognitive Complaints: Comparing the Relation between Self-Reported Versus Informant-Reported Subjective Cognitive Complaints and Cognitive Performances in Cognitively Unimpaired, Mild Cognitive Impairment and Populations with Dementia.

Subjective cognitive decline is proposed to be associated with future mild cognitive impairment and dementia. A better understanding of the roles of self-reported and informant-reported subjective cognitive complaints can provide a more delicate picture in dementia recognition and early diagnosis. To evaluate the accuracy of self-reported and informant-reported subjective cognitive complaints and the relation of subjective cognitive complaints and neuropsychological function in cognitively unimpaired, mild cognitive impairment and populations with dementia. We conducted a cross-sectional survey and evaluate the relations between subjective cognitive complaint scores and cognitive function in the different diagnostic groups. We recruited individuals diagnosed with cognitively unimpaired or mild cognitive impairment or dementia with Alzheimer's clinical syndrome from a memory clinic in a tertiary medical center in Taiwan. Participants, age greater than 50 years old, were enrolled in this study. Participants' informants were also enrolled for the cognitive questionnaire assessment. Participants' and informants' subjective cognitive complaint scores were collected based on a 12-item questionnaire. Neuropsychological assessments of global cognitive function, memory, language, executive function, visuospatial function and calculation were performed. The relations between subjective cognitive complaint scores and cognitive function in the different diagnostic groups were assessed by linear regression model. There were 1536 individuals and 1028 informants enrolled in this study. Self-reported subjective cognitive complaint scores from early and late mild cognitive impairment and dementia with Alzheimer's clinical syndrome participants showed no significant differences, but informants' subjective cognitive complaint scores showed a significant increase. Informant-reported subjective cognitive complaint scores related to neuropsychological tests in population with dementia. Neither self-reported nor informant-reported subjective cognitive complaint scores related to neuropsychological tests in cognitively unimpaired and mild cognitive impairment populations. Self-reported subjective cognitive complaints alone may not be sufficient to demonstrate clinical significance in different stages of cognitive impairment. Incorporating informant-reported subjective cognitive complaints, along with considering individual's anxiety and depressive status, are crucial in assessing cognitive statuses in clinical practice.

Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer's Disease Using Voice.

There is currently no simple, widely available screening method for Alzheimer's disease (AD), partly because the diagnosis of AD is complex and typically involves expensive and sometimes invasive tests not commonly available outside highly specialized clinical settings. Here, we developed an artificial intelligence (AI)-powered end-to-end system to detect AD and predict its severity directly from voice recordings. At the core of our system is the pre-trained data2vec model, the first high-performance self-supervised algorithm that works for speech, vision, and text. Our model was internally evaluated on the ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech only) dataset containing voice recordings of subjects describing the Cookie Theft picture, and externally validated on a test dataset from DementiaBank. The AI model can detect AD with average area under the curve (AUC) of 0.846 and 0.835 on held-out and external test set, respectively. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.9616). Moreover, the model can reliably predict the subject's cognitive testing score solely based on raw voice recordings. Our study demonstrates the feasibility of using the AI-powered end-to-end model for early AD diagnosis and severity prediction directly based on voice, showing its potential for screening Alzheimer's disease in a community setting.

What does it take to get a diagnosis? Dementia recognition and diagnosis pathways among Indigenous peoples

AbstractBackgroundPatients, caregivers, and providers face an oftentimes‐confusing healthcare terrain around dementia diagnostics and care given the lack of standardization for screening and evaluation of neurocognitive disorders. This can be further compounded by systemic healthcare inequalities and historical and present‐day marginalization faced by Indigenous populations.MethodWe present a segment of a pilot study (NIH R56 AG 62307) which sought to explore the impact of Alzheimer’s disease and related dementias (ADRDs) in Indigenous populations across four diverse settings, three in the United States and one in Canada. Community‐based researchers conducted key informant interviews with healthcare providers (n = 20) and sequential focus groups (SFGs) with local health care staff and formal caregivers (14 sessions, n = 17). Data were coded based on research questions and objectives in consultation with project leaders, coders, and community‐based researchers.ResultIn recognizing a cognitive change, all participants identified the importance of family members who often brought their concerns to the attention of a healthcare provider. Additionally, participants in the US and Canada identified events like hospital admissions as a “catastrophic event” (e.g., injury, missed medication, physical decline) as another route for diagnosis. A unique pathway that Canadian participants identified was the role of personal support workers (PSWs) and community nurses in noticing cognitive changes among individuals they served. Finally, providers mentioned various pathways for receiving a dementia diagnosis. While all providers mentioned taking steps to rule out other reasons for cognitive decline, the specific next step for the diagnosis varied widely. The possible professionals to see for a diagnosis included social workers, family doctors, tribal dementia care specialists, behavioral health specialists, psychiatrists, and neurologists.ConclusionFamily members are crucial in recognizing the changes associated with ADRDs. A strength of the Canadian healthcare system is that PSWs and community nurses can also help identify changes, potentially before hazardous “catastrophic events” bring people to the hospital. Once a cognitive change has been identified, the diagnostic pathway is complicated, non‐standardized, and irregular – in other words difficult to navigate. Policies and procedures need to be put in place, delineating standardized protocols to better help family caregivers and patients dealing with ADRDs.

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.

On becoming a dementia-friendly community: An empirical study of the individual factors that predict openness towards dementia-friendly communities.

Evidence suggests that limited public awareness and dementia-related stigma can create barriers to community inclusiveness. This study explored the relation between public knowledge of dementia, attitudes towards people with dementia, experience with dementia and openness towards dementia-friendly communities. Two-hundred and twenty-nine members of the public completed a cross-sectional online survey with three established measures [Alzheimer's Disease Knowledge Test, Attitudes Towards People with Dementia scale and Understanding of Health Problems survey] and one exploratory scale [Openness Towards Dementia-Friendly Communities]. An existing 'mild' dementia vignette was used to evaluate dementia recognition. Factors contributing to correct dementia recognition (knowledge, exposure to dementia, education and gender), worry about developing dementia (knowledge, exposure and respondent age), and openness towards dementia-friendly communities (knowledge, exposure and attitudes) were examined through group comparisons and a multiple linear regression. Consistent with the literature, the respondents had poor knowledge, reasonably good dementia recognition, and mostly positive attitudes towards people with dementia. None of the proposed factors were significantly associated with correct dementia recognition, and only gender was significantly associated with dementia worry. Knowledge and attitudes towards people with dementia (not exposure) were significant independent predictors of openness towards dementia-friendly communities. The expansion of dementia-friendly communities will require further investment in awareness-raising initiatives to improve dementia knowledge and attitudes in the community. This study shows the importance of these factors in the broader community's openness towards dementia-friendly communities.

A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing.

Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets.

COUNSELING ON DEMENTIA, STRESS, AND STRESS REGULATION AMONG MALE PRISON INMATES IN PALU’S CITY

Detention has become increasingly isolated since the Corona Virus Diseases 2019 (COVID-19) pandemic, as policies to prevent COVID-19 transmission have emerged. This may cause increased stress among inmates. Some research evidence links stress to poor health, memory loss, Alzheimer's, and dementia in general. Intrinsic and extrinsic stress factors are likely to persist as long as inmates are incarcerated (prison). Thus, stress, dementia, and stress regulation counseling and training are required.We educate inmates at the Palu male prison about dementia recognition, prevention, stress, and stress regulation.

Interpretable Recognition for Dementia Using Brain Images

Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.

Automatic Detection of Alzheimer's Disease Using Spontaneous Speech Only.

Alzheimer's disease (AD) is a neurodegenerative syndrome which affects tens of millions of elders worldwide. Although there is no treatment currently available, early recognition can improve the lives of people with AD and their caretakers and families. To find a cost-effective and easy-to-use method for dementia detection and address the dementia classification task of InterSpeech 2021 ADReSSo (Alzheimer's' Dementia Recognition through Spontaneous Speech only) challenge, we conduct a systematic comparison of approaches to detection of cognitive impairment based on spontaneous speech. We investigated the characteristics of acoustic modality and linguistic modality directly based on the audio recordings of narrative speech, and explored a variety of modality fusion strategies. With an ensemble over top-10 classifiers on the training set, we achieved an accuracy of 81.69% compared to the baseline of 78.87% on the test set. The results suggest that although transcription errors will be introduced through automatic speech recognition, integrating textual information generally improves classification performance. Besides, ensemble methods can boost both the accuracy and the robustness of models.

Crossing the “Cookie Theft” Corpus Chasm: Applying What BERT Learns From Outside Data to the ADReSS Challenge Dementia Detection Task

Large amounts of labeled data are a prerequisite to training accurate and reliable machine learning models. However, in the medical domain in particular, this is also a stumbling block as accurately labeled data are hard to obtain. DementiaBank, a publicly available corpus of spontaneous speech samples from a picture description task widely used to study Alzheimer's disease (AD) patients' language characteristics and for training classification models to distinguish patients with AD from healthy controls, is relatively small—a limitation that is further exacerbated when restricting to the balanced subset used in the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge. We build on previous work showing that the performance of traditional machine learning models on DementiaBank can be improved by the addition of normative data from other sources, evaluating the utility of such extrinsic data to further improve the performance of state-of-the-art deep learning based methods on the ADReSS challenge dementia detection task. To this end, we developed a new corpus of professionally transcribed recordings from the Wisconsin Longitudinal Study (WLS), resulting in 1366 additional Cookie Theft Task transcripts, increasing the available training data by an order of magnitude. Using these data in conjunction with DementiaBank is challenging because the WLS metadata corresponding to these transcripts do not contain dementia diagnoses. However, cognitive status of WLS participants can be inferred from results of several cognitive tests including semantic verbal fluency available in WLS data. In this work, we evaluate the utility of using the WLS ‘controls’ (participants without indications of abnormal cognitive status), and these data in conjunction with inferred ‘cases’ (participants with such indications) for training deep learning models to discriminate between language produced by patients with dementia and healthy controls. We find that incorporating WLS data during training a BERT model on ADReSS data improves its performance on the ADReSS dementia detection task, supporting the hypothesis that incorporating WLS data adds value in this context. We also demonstrate that weighted cost functions and additional prediction targets may be effective ways to address issues arising from class imbalance and confounding effects due to data provenance.

Recognition of Alzheimer’s Dementia From the Transcriptions of Spontaneous Speech Using fastText and CNN Models

Alzheimer’s dementia (AD) is a type of neurodegenerative disease that is associated with a decline in memory. However, speech and language impairments are also common in Alzheimer’s dementia patients. This work is an extension of our previous work, where we had used spontaneous speech for Alzheimer’s dementia recognition employing log-Mel spectrogram and Mel-frequency cepstral coefficients (MFCC) as inputs to deep neural networks (DNN). In this work, we explore the transcriptions of spontaneous speech for dementia recognition and compare the results with several baseline results. We explore two models for dementia recognition: 1) fastText and 2) convolutional neural network (CNN) with a single convolutional layer, to capture the n-gram-based linguistic information from the input sentence. The fastText model uses a bag of bigrams and trigrams along with the input text to capture the local word orderings. In the CNN-based model, we try to capture different n-grams (we usen= 2, 3, 4, 5) present in the text by adapting the kernel sizes to n. In both fastText and CNN architectures, the word embeddings are initialized using pretrained GloVe vectors. We use bagging of 21 models in each of these architectures to arrive at the final model using which the performance on the test data is assessed. The best accuracies achieved with CNN and fastText models on the text data are 79.16 and 83.33%, respectively. The best root mean square errors (RMSE) on the prediction of mini-mental state examination (MMSE) score are 4.38 and 4.28 for CNN and fastText, respectively. The results suggest that the n-gram-based features are worth pursuing, for the task of AD detection. fastText models have competitive results when compared to several baseline methods. Also, fastText models are shallow in nature and have the advantage of being faster in training and evaluation, by several orders of magnitude, compared to deep models.

A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition

In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP). We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.

Editorial: Alzheimer's Dementia Recognition through Spontaneous Speech.

Editorial: Alzheimer's Dementia Recognition through Spontaneous Speech.

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Alzheimer's dementia is a progressive neurodegenerative disease that causes cognitive and physical impairment. It severely deteriorates the quality of life in affected individuals. An early diagnosis can assist immensely in better management of their healthcare needs. In recent years, there has been a renewed impetus in development of automated methods for recognition of various disorders by leveraging advancements in artificial intelligence. Here, we propose a multimodal system that can identify linguistic and paralinguistic traits of dementia using an automated screening tool. We show that bag-of-deep-neural-embeddings and ensemble learning offer a viable approach to objective assessment of dementia. The developed system is tested on the Alzheimer's Dementia Recognition Challenge dataset, where it achieved a new state-of-the-art (SOTA) performance for the classification task and matched the current SOTA for the regression task. These results highlight the efficacy of our proposed system for facilitating an early diagnosis of dementia.