Predicting Alzheimer’s Disease

Straiton, Jenny

doi:10.2144/btn-2019-0114

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Currently, standard AD diagnosis methods include clinical assessments complemented with family history, neuropsychological tests (including the Mini-Mental State Examination (MMSE) [4] and many others), self-report questionnaires, MRI [5] and Positron Emission Tomography (PET) [6]. These methods are effective but are variously costly, invasive, timeconsuming and/or stressful, require validation by neurologists, and must be performed in clinical settings.…”

Section: Introductionmentioning

confidence: 99%

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Nasreen¹,

Hough²,

Purver³

2021

Interspeech 2021

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Alzheimer's Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focuses on analysis using linguistic features; here, we focus on non-linguistic features and their use in distinguishing AD patients from similar-age Non-AD patients with other health conditions in the Carolinas Conversation Collection (CCC) dataset. We used two types of features: patterns of interaction including pausing behaviour and floor control, and acoustic features including pitch, amplitude, energy, and cepstral coefficients. Fusion of the two kinds of features, combined with feature selection, obtains very promising classification results: classification accuracy of 90% using standard models such as support vector machines and logistic regression. We also obtain promising results using interactional features alone (87% accuracy), which can be easily extracted from natural conversations in daily life and thus have the potential for future implementation as a noninvasive method for AD diagnosis and monitoring.

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Section: Introductionmentioning

confidence: 99%

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Nasreen¹,

Hough²,

Purver³

2021

Interspeech 2021

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The Path to Solving the Protein Folding Problem

Straiton

2023

BioTechniques

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Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

Nasreen

Rohanian

Hough

et al. 2021

Front. Comput. Sci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder mainly characterized by memory loss with deficits in other cognitive domains, including language, visuospatial abilities, and changes in behavior. Detecting diagnostic biomarkers that are noninvasive and cost-effective is of great value not only for clinical assessments and diagnostics but also for research purposes. Several previous studies have investigated AD diagnosis via the acoustic, lexical, syntactic, and semantic aspects of speech and language. Other studies include approaches from conversation analysis that look at more interactional aspects, showing that disfluencies such as fillers and repairs, and purely nonverbal features such as inter-speaker silence, can be key features of AD conversations. These kinds of features, if useful for diagnosis, may have many advantages: They are simple to extract and relatively language-, topic-, and task-independent. This study aims to quantify the role and contribution of these features of interaction structure in predicting whether a dialogue participant has AD. We used a subset of the Carolinas Conversation Collection dataset of patients with AD at moderate stage within the age range 60–89 and similar-aged non-AD patients with other health conditions. Our feature analysis comprised two sets: disfluency features, including indicators such as self-repairs and fillers, and interactional features, including overlaps, turn-taking behavior, and distributions of different types of silence both within patient speech and between patient and interviewer speech. Statistical analysis showed significant differences between AD and non-AD groups for several disfluency features (edit terms, verbatim repeats, and substitutions) and interactional features (lapses, gaps, attributable silences, turn switches per minute, standardized phonation time, and turn length). For the classification of AD patient conversations vs. non-AD patient conversations, we achieved 83% accuracy with disfluency features, 83% accuracy with interactional features, and an overall accuracy of 90% when combining both feature sets using support vector machine classifiers. The discriminative power of these features, perhaps combined with more conventional linguistic features, therefore shows potential for integration into noninvasive clinical assessments for AD at advanced stages.

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Predicting Alzheimer’s Disease

Abstract: As the struggle to find a cure continues, could advances in diagnostic and predictive techniques be the way forward for improving our understanding of Alzheimer's disease?

Cited by 3 publications

References 5 publications

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

The Path to Solving the Protein Folding Problem

Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

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Predicting Alzheimer’s Disease

Abstract: As the struggle to find a cure continues, could advances in diagnostic and predictive techniques be the way forward for improving our understanding of Alzheimer's disease?

Cited by 3 publications

References 5 publications

Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech

The Path to Solving the Protein Folding Problem

Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

Contact Info

Product

Resources

About

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech