语音质量作为双极性障碍的数字生物标志物:系统性审查
Giovanni Briganti1, Jérôme R Lechien2
1Unit of Computational Medicine and Neuropsychiatry, Faculty of Medicine, Pharmacy and Biomedical Sciences, University of Mons (UMONS), Mons, Belgium; Department of Clinical Sciences, Faculty of Medicine, University of Liège, Liège, Belgium; Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium.
Journal of voice : official journal of the Voice Foundation
|January 16, 2025
概括
语音分析显示作为双相情绪障碍 (BD) 情绪检测的生物标志物具有前途. 机器学习模型准确地识别躁狂状态,将语音特征与临床尺度相关联,以进行个性化监测.
科学领域:
- 生物医学信号处理
- 精神病学是一个精神病学.
- 医疗保健中的机器学习
背景情况:
- 语音分析是双相情绪障碍 (BD) 中情绪状态检测的新兴生物标志物.
- 系统审查评估语音分析应用在 BD,专注于预测有效性和与临床尺度的相关性.
研究的目的:
- 总结关于语音分析的证据,以检测双相情绪障碍 (BD) 中的情绪状态.
- 评估语音质量对情绪状态检测的预测有效性.
- 检查语音参数和BD中的临床症状尺度之间的相关性.
主要方法:
- 在PubMed,Scopus和Cochrane图书馆进行系统的文献搜索.
- 包括16项与575名BD患者的研究,使用非随机化研究 (MINORS) 修改方法指数进行评估.
- 专注于机器学习方法在BD语音分析.
主要成果:
- 机器学习实现了70.9%-96.9%的准确性;躁狂状态检测显示出强大的预测有效性 (AUC高达0.89).
- 个体特定模型的表现优于人口模型 (0.78与0.44相关性).
- 语音参数与青年躁狂评分表和汉密尔顿抑郁症评分表有显著的相关性;在躁狂中注意到高音位.
结论:
- 语音质量是BD的一个有前途的生物标志物,特别是用于躁狂状态的检测和个性化监测.
- 控制的设置表现出强的性能,但自然主义应用更为温和.
- 未来的研究需要标准化的协议和大规模的纵向研究.
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