彭敏,张亚明,樊永梅,等.基于自然语言处理技术的快速筛查在中国中老年人群轻度认知障碍中的应用[J].中华物理医学与康复杂志,2023,45(7):592-597
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| 基于自然语言处理技术的快速筛查在中国中老年人群轻度认知障碍中的应用 |
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| DOI：10.3760/cma.j.issn.0254-1424.2023.07.003 |
| 中文关键词: 阿尔茨海默病 痴呆症 轻度认知障碍 自然语言处理 筛查 |
| 英文关键词: Alzheimer′s disease Dementia Speech analysis Cognitive impairment Natural language processing Screening |
| 基金项目:中南大学湘雅二医院横向课题（0858.210903），湖南省残疾人联合会康复科研项目（2021XK0306），湖南省自然科学基金青年基金（2020JJ5803） |
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| 中文摘要: |
| 目的 采用自然语言处理（NLP）技术，通过特定语音任务在中国中老年人群中自动、客观、快速检测轻度认知障碍（MCI）。 方法 以1∶1的男女比例招募了50～80岁中老年人215例，用特定的语音任务和简易精神状态测试-2（MMSE-2）收集受试者的语音数据和认知功能情况，并根据其认知功能进一步分为认知功能正常组和MCI组。根据语音文件提取出说话速度、音节数、音节时间长度、停顿数、停顿时间长度、共振峰频率 (F1、F2) 的标准偏差、声压变化共7类，合计162个语音特征，依据男性和女性分组进行分析，比较不同性别受试者的语音特征与认知功能的关联，用多元回归分别分析男、女受试者基于语音特征的认知功能预测，并使用灵敏度、特异度和准确度评价模型的预测功能。 结果 MCI组受试者在发音速度、停顿次数、停顿长度及共振峰变化的50个语音特征量上与正常组比较，差异有统计学意义（P＜0.05）。不同性别的分组分析中，单相关分析表明，发音节奏与认知功能显著相关；基于多元回归构建的预测模型中，识别MCI的敏感度、特异度和准确度男性分别为0.54、0.80和0.69，女性分别为0、0.86和0.63。 结论 MCI组患者的发音节奏发生了显著改变。基于自然语言处理技术的语音分析能快速、客观地筛查出MCI。 |
| 英文摘要: |
| Objective To automatically and rapidly detect mild cognitive impairment (MCI) in an objective manner using natural language processing (NLP). Methods A total of 215 participants (half female) aged 50 to 80 were recruited for the study′s normal cognition and MCI groups. Speech tasks and the mini mental state examination (MMSE-2) were used to collect audio data and quantify cognitive functioning. Altogether 162 acoustic features were extracted including the speaking speed, syllable number, syllable duration, number of pauses, duration of pauses, the standard deviation of formant frequency and sound pressure variation. They were compared between the two groups and genders. Multiple regression analysis was used to formulate a model predicting MCI. The sensitivity, specificity and accuracy of its predictions were used to evaluate its predictive power. Results There were significant differences between the two groups in 50 acoustic features including their pronunciation rhythm and pronunciation accuracy. Univariate correlation analysis revealed that the pronunciation rhythm was significantly associated with cognitive functioning. The sensitivity, specificity and accuracy of the model were 0.54, 0.80 and 0.69 for males and 0.00, 0.86 and 0.63 for females. Conclusion MCI greatly affects pronunciation rhythm. Acoustic analysis based on NLP can detect MCI rapidly and objectively. |
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