Non-transcription analysis of connected speech in mild cognitive impairment using an information unit scoring system

2022 ◽  
Vol 61 ◽  
pp. 101035
Author(s):  
Hana Kim ◽  
Jee Eun Sung ◽  
Jee Hyang Jeong
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Scoring System ◽  
Connected Speech ◽  
Transcription Analysis ◽  
Information Unit

scholarly journals Development of a Sex-Specific Risk Scoring System for Predicting Cognitive Normal to Mild Cognitive Impairment (SRSS-CNMCI)

2021 ◽  
Author(s):  
Wen Luo ◽  
Hao Wen ◽  
Shuqi Ge ◽  
Chunzhi Tang ◽  
Xiufeng Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
High Risk ◽  
Scoring System ◽  
External Validation ◽  
Scoring Systems ◽  
Cutoff Point ◽  
Risk Scoring ◽  
Risk Scoring System

Abstract Objective: We aim to develop a sex-specific risk scoring system for predicting cognitive normal (CN) to mild cognitive impairment (MCI), abbreviated SRSS-CNMCI, to provide a reliable tool for the prevention of MCI.Methods: Participants aged 61-90 years old with a baseline diagnosis of CN and an endpoint diagnosis of MCI were screened from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with at least one follow-up. Multivariable Cox proportional hazards models were used to identify risk factors associated with conversion from CN to MCI and to build risk scoring systems for male and female groups. Receiver operating characteristic (ROC) curve analysis was applied to determine the risk probability cutoff point corresponding to the optimal prediction effect. We ran an external validation of the discrimination and calibration based on the Harvard Aging Brain Study (HABS) database.Results: A total of 471 participants, including 240 women (51%) and 231 men (49%), aged 61 to 90 years, were included in the study cohort for subsequent primary analysis. The final multivariable models and the risk scoring systems for females and males included age, APOE ε4, Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). The scoring systems for females and males revealed C statistics of 0.902 (95% CI 0.840-0.963) and 0.911 (95% CI 0.863-0.959), respectively, as measures of discrimination. The cutoff point of high and low risk was 33% in females, and more than 33% was considered high risk, while more than 9% was considered high risk for males. The external validation effect of the scoring systems was good: C statistic 0.950 for the females and C statistic 0.965 for the males. Conclusions: Our parsimonious model accurately predicts conversion from CN to MCI with four risk factors and can be used as a predictive tool for the prevention of MCI.

A System for Quantifying the Informativeness and Efficiency of the Connected Speech of Adults With Aphasia

1993 ◽  
Vol 36 (2) ◽  
pp. 338-350 ◽  
Author(s):  
Linda E. Nicholas ◽  
Robert H. Brookshire
Keyword(s):  
Scoring System ◽  
Connected Speech ◽  
Rule Based ◽  
Individual Basis ◽  
Information Unit ◽  
Correct Information ◽  
Significant Difference ◽  
Interjudge Reliability

A standardized rule-based scoring system, the Correct Information Unit (CIU) analysis, was used to evaluate the informativeness and efficiency of the connected speech of 20 non-brain-damaged adults and 20 adults with aphasia in response to 10 elicitation stimuli. The interjudge reliability of the scoring system proved to be high, as did the session-to-session stability of performance on measures. There was a significant difference between the non-brain-damaged and aphasic speakers on each of the five measures derived from CIU and word counts. However, the three calculated measures (words per minute, percent CIUs, and CIUs per minute) more dependably separated aphasic from non-brain-damaged speakers on an individual basis than the two counts (number of words and number of CIUs).

Clock Drawing Test: A Simple Scoring System for the Accurate Screening of Cognitive Impairment in Patients with Mild Cognitive Impairment and Dementia

2018 ◽  
Vol 45 (5-6) ◽  
pp. 326-334 ◽  
Author(s):  
Martin Rakusa ◽  
Joze Jensterle ◽  
Janez Mlakar
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Scoring System ◽  
Mini Mental State Examination ◽  
Screening Method ◽  
Mixed Dementia ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test ◽  
State Examination

Background/Aim: The Clock Drawing Test (CDT) is a valid alternative screening tool to the Mini-Mental State Examination (MMSE) and, crucially, it may be completed faster. The aim of our study was to standardize and simplify the CDT scoring system for screening in three common conditions: mild cognitive impairment (MCI), Alzheimer’s disease (AD) and mixed dementia (MD). Methods: We included 188 subjects (43 healthy volunteers, 49 patients with MCI, 54 patients with AD, and 42 patients with MD), who performed the MMSE and CDT. The CDT was evaluated using a modified 4-point scoring system. Results: The healthy subjects had the highest median values for the MMSE and CDT, followed by patients with MCI, AD and MD. The optimal cut-off for all patients and each patient group separately was 3 out of 4 points. Sensitivity was 89% for AD, 93% for MD and 83% for all patients, while specificity was 91%. The MMSE produced similar results. In comparison to the MMSE, sensitivity for MCI was significantly higher using the CDT (20 vs. 69%, respectively). Conclusion: A simple, 4-point scoring system may be used as a screening method for fast and accurate detection of cognitive impairment in patients with MCI, AD and MD.

scholarly journals A Comparison of Connected Speech Tasks for Detecting Early Alzheimer’s Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning

2021 ◽  
Vol 3 ◽  
Author(s):  
Natasha Clarke ◽  
Thomas R. Barrick ◽  
Peter Garrard
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Natural Language Processing ◽  
Mild Cognitive Impairment ◽  
Natural Language ◽  
Language Processing ◽  
Picture Book ◽  
Connected Speech ◽  
Picture Description ◽  
Description Task

Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.

“2-step MCI-AD”: a simple scoring system to predict rapid conversion from mild cognitive impairment to Alzheimer dementia

2021 ◽  
Vol 94 ◽  
pp. 104359
Author(s):  
Antonio Muscari ◽  
Fabio Clavarino ◽  
Vincenzo Allegri ◽  
Andrea Farolfi ◽  
Maria Macchiarulo ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Scoring System ◽  
Alzheimer Dementia ◽  
Rapid Conversion

Connected speech assessment in the early detection of Alzheimer’s disease and mild cognitive impairment: a scoping review

Aphasiology ◽  
2019 ◽  
Vol 34 (6) ◽  
pp. 723-755 ◽  
Author(s):  
Renée-Pier Filiou ◽  
Nathalie Bier ◽  
Antoine Slegers ◽  
Bérengère Houzé ◽  
Patricia Belchior ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Scoping Review ◽  
Connected Speech ◽  
Speech Assessment
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