利用语音衍生的数字生物标志物来检测和量化老年人认知能力下降的严重程度
Gozde Cay1, Valeria A Pfeifer2, Myeounggon Lee3
1Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA, Gozde.Cay@bcm.edu.
Gerontology
|January 14, 2024
概括
数字语音分析提供了一种新的方式来检测老年人的认知障碍. 语音生物标志物,如时间和相似性,可以准确地识别障碍及其严重程度,帮助早期诊断和痴呆症护理.
科学领域:
- 老年学是一门学科.
- 神经学 神经学
- 语音语言病理学 语言病理学
背景情况:
- 目前的认知评估面临诸如实践效应和轻度病例表现不佳等局限性.
- 数字语音分析为认知障碍评估提供了一个有希望的替代方案.
研究的目的:
- 识别用于检测认知障碍及其严重程度的数字语音生物标志物.
- 探索语音分析作为一种用于早期发现和监测认知衰退的工具.
主要方法:
- 招募了具有不同认知健康状况的老年人,在阅读段落时录制语音.
- 获得数字语音生物标志物 (时机,音调,声音强度) 并分析与蒙特利尔认知评估 (MoCA) 成绩的相关性.
- 利用随机森林模型来区分认知状态并预测MoCA分数.
主要成果:
- 语音相似性 (动态时间扭曲) 和时间参数 (额外单词比率) 与MoCA分数有显著的相关性.
- 四个语音参数的组合在区分认知状态时实现了88.1%的精度和76.3%的平衡精度.
- 来自阅读的语音数据有效地区分了认知障碍和完整的老年人.
结论:
- 语音分析,特别是时间和相似度指标,可以作为认知障碍的可行的数字生物标志物.
- 这种方法为早期检测,监测和新的痴呆症护理策略提供了潜力.
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