Diagnosis and Treatment Methods in Alzheimer's Patients Based on Modern Techniques: The Orginal Article

doi:10.47750/pnr.2022.13.s01.226

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…This suggests that the observed differences in the "frustrated" emotion between groups are not driven by temporal variation but rather reflect inherent distinctions between the groups. It is possible that the decline in formal language abilities experienced by participants contributes to a sense of loss of control, which can lead to their frustration and anger [63], [64]. These may contribute to the subtle differences in the "frustrated" emotion percentage observed.…”

Section: Health Statusmentioning

confidence: 99%

Exploring Emotion and Emotional Variability as DigitalBiomarkers in Frontotemporal Dementia Speech

Gong,

Parllaku,

Placek

et al. 2024

IEEE Access

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Frontotemporal Dementia (FTD) encompasses a diverse group of progressive neurodegenerative diseases that impact speech production and comprehension, higher-order cognition, behavior, and motor control. Traditional acoustic speech markers have been extensively studied in FTD, as have assessments capturing apathy and impairments in recognizing and expressing emotion. This work leverages machine learning to track changes in emotional content within the speech of individuals with FTD and healthy controls. The aim of the project is to develop tools for assessing and monitoring emotional changes in individuals with FTD, quantifying these subtle aspects of the disease and thus potentially providing insights for assessing future therapeutic interventions. A retrospective analysis was conducted on a dataset comprising standard elicited speech tasks performed by 78 individuals diagnosed with FTD and 55 healthy elderly controls. We employed an ensemble-based convolutional neural network (CNN) classifier trained on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset to extract emotion scores from processed speech samples. The classifier was applied with a sliding window to the FTD and healthy control narratives to facilitate a granular examination of emotional changes throughout longer speech samples. Analysis of variance (ANOVA) was used to test for group differences in average emotion scores as well as emotional variability over the duration of the speech samples. Compared to healthy controls, people with FTD demonstrated reduced emotional change in a monologue task describing a happy experience, as measured by the interquartile range (IQR) (p < 0.005) and slope of "happy" emotion scores vs. time (p < 0.005). During a picture description task, people with FTD displayed a slightly elevated average level of frustration (p < 0.005). Increased frustration levels in individuals with FTD could potentially indicate their difficulties in accomplishing the task. This study introduced the application of a pre-trained Speech Emotion Recognition (SER) model on overlapping short segments of extended speech samples, allowing for a detailed examination of emotional changes over time. Capturing the temporal evolution of emotional content offers a nuanced understanding of communication in individuals with FTD. Our findings lay the groundwork for further development of digital biomarkers to refine the assessment, monitoring, and understanding of the emotional and social communication impacts of FTD.

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Section: Health Statusmentioning

confidence: 99%

Exploring Emotion and Emotional Variability as DigitalBiomarkers in Frontotemporal Dementia Speech

Gong,

Parllaku,

Placek

et al. 2024

IEEE Access

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show abstract

“…In the twenty-first century, Alzheimer's disease is a common form of dementia and a healthcare concern. Memory loss, diminished cognitive ability, and difficulty with simple tasks are prominent indications of this condition [21]. Older patients have dementia [22], [23].…”

Section: C) Alzheimer's Diseasementioning

confidence: 99%

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

jain

2023

Preprint

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BrainWave Diagnostics, an emerging field, leverages electroencephalography (EEG) data for cost-effective and resource-efficient neurological disorder detection. Although EEGs are commonly used for neurological disease detection, their low signal intensity and nonlinear features pose analytical challenges. This review explores the use of high-performance computational tools, machine learning, and deep learning methods in diagnosing a range of neurological disorders, including epilepsy, Parkinson's disease, autism, ADHD, stroke, tumors, schizophrenia, Alzheimer's, depression, and alcohol use disorder. The increasing prevalence of neurological disorders and their resource burden underscores the urgency of these diagnostic advancements. Future research can consider multi-modal approaches, providing practical solutions for neurological disorder detection beyond EEGs, with potential applications in diverse signal analysis domains.

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“…Mild Cognitive Impairment (MCI) and Early Stages: In the early stages of AD, individuals often experience subtle changes in cognitive function that may be attributed to normal aging [24]. Common early symptoms include:…”

Section: Severe Dementiamentioning

confidence: 99%

Convolutional Neural Networks for Alzheimer’s Disease

2023

Preprint

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Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.

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Diagnosis and Treatment Methods in Alzheimer's Patients Based on Modern Techniques: The Orginal Article

Cited by 8 publications

References 24 publications

Exploring Emotion and Emotional Variability as DigitalBiomarkers in Frontotemporal Dementia Speech

Exploring Emotion and Emotional Variability as DigitalBiomarkers in Frontotemporal Dementia Speech

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

Convolutional Neural Networks for Alzheimer’s Disease

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