Oral reading fluency analysis in patients with Alzheimer disease and asymptomatic control subjects

Martínez‐Sánchez, Francisco; Meilán, Juan José García; García-Sevilla, Julia; Carro, Juan; Martínez, José María Arana

doi:10.1016/j.nrleng.2012.07.017

Cited by 17 publications

(20 citation statements)

References 25 publications

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“…Interestingly, they found that AD patients produce longer turns with more words and a higher speech rate; this contrasts with our results, in which AD patients produce fewer words than non-AD patients, with lower speech rates. We note that our findings align better with other research (Martínez-Sánchez et al, 2013;Kavé and Dassa, 2018;Pistono et al, 2019a;Themistocleous et al, 2020). Mirheidari et al (2019) went a step further, combining CA-inspired interaction features including turntaking behavior with some acoustic and language features, to achieve a classification accuracy of 90% similar to this study.…”

Section: Classification Results and Discussionsupporting

confidence: 93%

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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Section: Classification Results and Discussionsupporting

confidence: 93%

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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“…70Hz) [9]; (6) Standard deviation of the third formant (F3), which refers to tones between 1.5kHz and 2.5kHz; (7) Speech rate, i.e. the number of syllables divided by the total speech time [16]; (8) Mean duration of the syllables [16]; (9) Mean duration of the inter-syllabic pauses >250ms [16]; (10) Percentage of the phonation time, i.e. the intra-and intersyllabic nuclei time <250ms compared to the total speech time [16]; (1))Articulation rate, i.e.…”

Section: B Data Analysismentioning

confidence: 99%

“…the intra-and intersyllabic nuclei time <250ms compared to the total speech time [16]; (1))Articulation rate, i.e. number of syllables divided by the phonation time without pauses [16].…”

Section: B Data Analysismentioning

confidence: 99%

Automatic speech analysis to early detect functional cognitive decline in elderly population

Ambrosini

Caielli

Milis

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…AD patients represent the degree of deficits in specific cognitive constructs: neurophysiologic change following the progression of AD (e.g., presence of amyloid plaques, neurofibrillary tangles, and diffuse degeneration and atrophy of various parts of the cortex) can lead to changes in sensory perception and motor symptoms, resulting in impairment of spontaneous speech [17][18][19]. A stream of evidence has shown that AD patients are more likely to speak more slowly and with longer pauses, and spend more time finding the correct word, resulting in broken messages and lack of speech fluency [20][21][22]. These indicate the possibility of further developing further accurate prediction models using vocal features to identify AD risk [23,24].…”

Section: Introductionmentioning

confidence: 99%

Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model

Shimoda¹,

Li²,

Hayashi

et al. 2021

PLoS ONE

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Due to difficulty in early diagnosis of Alzheimer’s disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files’ predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794–0.931), 0.882 (95% CI: 0.840–0.924), and 0.893 (95%CI: 0.832–0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000–1.000), 1.000 (95%CI: 1.000–1.000), 0.972 (95%CI: 0.918–1.000) and 0.917 (95%CI: 0.918–1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.

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Oral reading fluency analysis in patients with Alzheimer disease and asymptomatic control subjects

Cited by 17 publications

References 25 publications

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

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

Automatic speech analysis to early detect functional cognitive decline in elderly population

Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model

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