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当代阿耳茨海默病研究

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

区分轻度认知障碍的认知测试的自动评估：数字跨度任务的概念研究

卷 17, 期 7, 2020

页: [658 - 666] 页: 9

弟呕挨: 10.2174/1567205017666201008110854

价格: $65

摘要

背景：当前的常规认知评估的效率和敏感性有限，通常依赖于单个分数，例如总正确项目。通常，响应的多个功能无法捕获。 目标：我们旨在探索从数字跨度（DS）任务自动获取的一组新功能，这些功能可解决常规评分中的一些缺陷，并且对于区分轻度认知障碍（MCI）和完整认知的受试者也非常有用。 方法：使用自动语音识别（ASR）系统转录对85位受试者（22位MCI和63位健康对照，平均年龄90.2岁）进行的DS测试的录音。接下来，通过对响应的Levenshtein距离分析生成了五个正确性度量：与测试项目相比，对正确，错误，删除，插入和替换的单词进行编号。这些摘要功能使用摘要统计功能在前向数字跨度（FDS）和后向数字跨度（BDS）任务的所有测试项目中汇总，构建了一个全局特征向量，表示对每个受试者反应的详细评估。支持向量机分类器将MCI与认知完好的参与者区分开。 结果：常规DS评分并未将MCI参与者与对照组区分开。自动化的多特征DS衍生指标在SVM分类器的AUC-ROC上达到了73％，而与其他临床特征无关（与受试者的人口统计学特征相结合时为77％）； 50％以上的机会。 结论：我们的分析验证了将MCI受试者与具有完整认知能力的受试者区分开的背景下，仅从DS任务中得出的措施的有效性。

关键词: 神经心理学测试，短期记忆，数字跨度，生物标志物，轻度认知障碍（MCI），计算机评估。

« Previous Next »
[1]
Asgari M, Kaye J, Dodge H. Predicting mild cognitive impairment from spontaneous spoken utterances. Alzheimers Dement 2017; 3(2): 219-28.
[http://dx.doi.org/10.1016/j.trci.2017.01.006] [PMID: 29067328]
[2]
Lopez-de-Ipina K, Martinez-de-Lizarduy U, Calvo PM, et al. Advances on automatic speech analysis for early detection of Alzheimer disease: A non-linear multi-task approach. Current Alzheimer Research. Curr Alzheimer Res 2018; 15(2): 139-48.
[http://dx.doi.org/10.2174/1567205014666171120143800] [PMID: 29165084]
[3]
Tóth L, Hoffmann I, Gosztolya G, et al. A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech. Curr Alzheimer Res 2018; 15(2): 130-8.
[http://dx.doi.org/10.2174/1567205014666171121114930] [PMID: 29165085]
[4]
Meilán JJ, Martínez-Sánchez F, Martínez-Nicolás I, Llorente TE, Carro J. Changes in the rhythm of speech difference between people with nondegenerative mild cognitive impairment and with preclinical dementia. Behav Neurol 2020; 20204683573
[5]
Roark B, Mitchell M, Hosom J-P, Hollingshead K, Kaye J. Spoken language derived measures for detecting mild cognitive impairment. IEEE Trans Audio Speech Lang Process 2011; 19(7): 2081-90.
[http://dx.doi.org/10.1109/TASL.2011.2112351]
[6]
Humpstone H. Memory span tests. The psychological clinic. Biomed J Digitization Proj 1919; 12(5-9): 196.
[7]
Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53(4): 695-9.
[http://dx.doi.org/10.1111/j.1532-5415.2005.53221.x] [PMID: 15817019]
[8]
Osmon DC, Smet IC, Winegarden B, Gandhavadi B. Neurobehavioral cognitive status examination: Its use with unilateral stroke patients in a rehabilitation setting. Arch Phys Med Rehabil 1992; 73(5): 414-8.
[PMID: 1580766]
[9]
Tariq SH, Tumosa N, Chibnall JT, Perry MH III, Morley JE. Comparison of the Saint Louis University mental status examination and the mini-mental state examination for detecting dementia and mild neurocognitive disorder-a pilot study. Am J Geriatr Psychiatry 2006; 14(11): 900-10.
[10]
Richardson JT. Measures of short-term memory: A historical review. Cortex 2007; 43(5): 635-50.
[http://dx.doi.org/10.1016/S0010-9452(08)70493-3] [PMID: 17715798]
[11]
Banken JA. Clinical utility of considering Digits Forward and Digits Backward as separate components of the wechsler adult intelligence Scale-Revised. J Clin Psychol Wiley Online Library 1985; 41(5): 686-91.
[http://dx.doi.org/10.1002/1097-4679(198509)41:5<686::AIDJCLP2270410517> 3.0.CO;2-D]
[12]
Carlesimo GA, Fadda L, Lorusso S, Caltagirone C. Verbal and spatial memory spans in Alzheimer’s and multi-infarct dementia. Acta Neurol Scand 1994; 89(2): 132-8.
[http://dx.doi.org/10.1111/j.1600-0404.1994.tb01648.x] [PMID: 8191876]
[13]
Morris JC, Ernesto C, Schafer K, Coats M, Leon S, Sano M. Clinical dementia rating training and reliability in multicenter studies the Alzheimer’s disease cooperative study experience neurology. AAN Enterprises 1997; 48(6): 1508.
[14]
Kiernan RJ, Mueller J, Langston JW, Van Dyke C. The Neurobehavioral Cognitive Status Examination: A brief but differentiated approach to cognitive assessment. Am Coll Physicians 1987; 107(4): 481-5.
[PMID: 3631786]
[15]
Morris JC. The Clinical Dementia Rating (CDR): Current version and scoring rules Neurology. Lippincott Williams & Wilkins 1993.
[16]
Cockrell JR, Folstein MF. Mini-mental state examination Principles and practice of geriatric psychiatry. Citeseer 2002; pp. 140-1.
[http://dx.doi.org/10.1002/0470846410.ch27(ii)]
[17]
Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput 2004; 14(3): 199-222.
[http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88]
[18]
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12: 2825-30.
[19]
Schölkopf B, Smola AJ. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press 2002.
[20]
Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: Data mining, inference and prediction. Math Intelligencer 2005; 27(2): 83-5.
[21]
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143(1): 29-36.
[http://dx.doi.org/10.1148/radiology.143.1.7063747] [PMID: 7063747]
[22]
Povey D, Ghoshal A, Boulianne G, Burget L, Glembek O, Goel N. The Kaldi speech recognition toolkit. IEEE 2011 workshop on automatic speech recognition and understanding IEEE Signal Processing Society 2011.
[23]
Vipperla R, Renals S, Frankel J. Ageing voices: The effect of changes in voice parameters on ASR performance. EURASIP J Audio Speech and Music Processing 2010.

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