Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.

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Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.712251 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yasunori Yamada ◽

Kaoru Shinkawa ◽

Miyuki Nemoto ◽

Tetsuaki Arai

Keyword(s):

Older Adults ◽

Early Detection ◽

Daily Life ◽

Binary Classification ◽

Emotional States ◽

Automatic Assessment ◽

Adverse Health Effects ◽

Wide Range ◽

Speech Data ◽

Speech Features

Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored. In this study, we developed a tablet-based application and collected speech responses of 57 older adults to daily life questions regarding, for example, one's feelings and future travel plans. From audio data of these speech responses, we automatically extracted speech features characterizing acoustic, prosodic, and linguistic aspects, and investigated their associations with self-rated scores of the UCLA Loneliness Scale. Consequently, we found that with increasing loneliness scores, speech responses tended to have less inflections, longer pauses, reduced second formant frequencies, reduced variances of the speech spectrum, more filler words, and fewer positive words. The cross-validation results showed that regression and binary-classification models using speech features could estimate loneliness scores with an R2 of 0.57 and detect individuals with high loneliness scores with 95.6% accuracy, respectively. Our study provides the first empirical results suggesting the possibility of using speech data that can be collected in everyday life for the automatic assessments of loneliness in older adults, which could help develop monitoring technologies for early detection and intervention for mitigating loneliness.

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Early Detection and Differential Diagnosis of Alzheimer’s Disease and Depression with Neuropsychological Tasks

Dementia and Geriatric Cognitive Disorders ◽

10.1159/000051269 ◽

2001 ◽

Vol 12 (4) ◽

pp. 265-280 ◽

Cited By ~ 245

Author(s):

R. Swainson ◽

J.R. Hodges ◽

C.J. Galton ◽

J. Semple ◽

A. Michael ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differential Diagnosis ◽

Early Detection ◽

Neuropsychological Tasks

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Verbal Memory Test Best Indicator of Who Will Have Alzheimer's Disease, New Study Says: Early Detection of Alzheimer's Could Lessen the Impact

PsycEXTRA Dataset ◽

10.1037/e365012004-001 ◽

2002 ◽

Author(s):

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Detection ◽

Verbal Memory ◽

Memory Test ◽

The Impact

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Exploring the possibility for early detection of Alzheimer's disease with spatial-domain neural images

2020 20th International Conference on Control, Automation and Systems (ICCAS) ◽

10.23919/iccas50221.2020.9268361 ◽

2020 ◽

Author(s):

Dalin Yang ◽

Ruisen Huang ◽

Kunqiang Qing ◽

Keum-Shik Hong

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Detection ◽

Spatial Domain

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Development and Evaluation of a Tactile Cognitive Function Test Device for Alzheimer’s Disease Early Detection

Neuroscience and Biomedical Engineering ◽

10.2174/2213385203666150804001349 ◽

2015 ◽

Vol 3 (2) ◽

pp. 58-65 ◽

Cited By ~ 2

Author(s):

Jiajia Yang ◽

Mohd Usairy Syafiq ◽

Yinghua Yu ◽

Satoshi Takahashi ◽

Zhenxin Zhang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Function ◽

Early Detection ◽

Test Device ◽

Function Test ◽

Cognitive Function Test

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Remote monitoring technologies in Alzheimer’s disease: design of the RADAR-AD study

Alzheimer s Research & Therapy ◽

10.1186/s13195-021-00825-4 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Marijn Muurling ◽

◽

Casper de Boer ◽

Rouba Kozak ◽

Dorota Religa ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Remote Monitoring ◽

Rating Scales ◽

Functional Decline ◽

Cognitive Domain ◽

Subjective Rating ◽

Functional Domain ◽

Smartphone Applications ◽

Monitoring Technologies

Abstract Background Functional decline in Alzheimer’s disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales. Methods This study includes three tiers. For the main study, we will include participants (n = 220) with preclinical AD, prodromal AD, mild-to-moderate AD, and healthy controls, classified by MMSE and CDR score, from clinical sites equally distributed over 13 European countries. Participants will undergo extensive neuropsychological testing and physical examination. The RMT assessments, performed over an 8-week period, include walk tests, financial management tasks, an augmented reality game, two activity trackers, and two smartphone applications installed on the participants’ phone. In the first sub-study, fixed sensors will be installed in the homes of a representative sub-sample of 40 participants. In the second sub-study, 10 participants will stay in a smart home for 1 week. The primary outcome of this study is the difference in functional domain profiles assessed using RMTs between the four study groups. The four participant groups will be compared for each RMT outcome measure separately. Each RMT outcome will be compared to a standard clinical test which measures the same functional or cognitive domain. Finally, multivariate prediction models will be developed. Data collection and privacy are important aspects of the project, which will be managed using the RADAR-base data platform running on specifically designed biomedical research computing infrastructure. Results First results are expected to be disseminated in 2022. Conclusion Our study is well placed to evaluate the clinical utility of RMT assessments. Leveraging modern-day technology may deliver new and improved methods for accurately monitoring functional decline in all stages of AD. It is greatly anticipated that these methods could lead to objective and real-life functional endpoints with increased sensitivity to pharmacological agent signal detection.

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Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications ◽

10.1007/s00521-021-06105-4 ◽

2021 ◽

Author(s):

Antonio Giovannetti ◽

Gianluca Susi ◽

Paola Casti ◽

Arianna Mencattini ◽

Sandra Pusil ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Brain Regions ◽

Ensemble Classification ◽

The Novel ◽

Ensemble Classifiers ◽

Deep Convolutional Neural Networks ◽

Early Signs ◽

Data Representations ◽

The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

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Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Applied Sciences ◽

10.3390/app11041574 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1574

Author(s):

Shabana Urooj ◽

Satya P. Singh ◽

Areej Malibari ◽

Fadwa Alrowais ◽

Shaeen Kalathil

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Detection ◽

Differential Evolution ◽

Early Stage ◽

Selection Process ◽

Parameter Tuning ◽

Wavelet Neural Network ◽

Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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