通过声音检测抑郁和躁狂情绪状态,作为使用机器学习的生物标志物
Jun Ji1,2, Wentian Dong3, Jiaqi Li4
1College of Computer Science and Technology, Qingdao University, Qingdao, China.
Frontiers in neurology
|July 19, 2024
概括
语音分析显示,它有望用于检测情绪障碍. 机器学习模型可以使用语音生物标志物区分抑郁和躁狂状态,提供更客观的评估工具.
科学领域:
- 精神病学是一个精神病学.
- 计算语言学 计算语言学
- 生物医学工程 生物医学工程
背景情况:
- 抑郁和躁狂状态对全球健康构成重大负担.
- 目前缺乏情绪障碍的客观诊断工具.
- 语音分析为情绪状态检测提供了一个潜在的非侵入性生物标志物.
研究的目的:
- 调查使用语音录音作为检测抑郁和躁狂状态的生物标志物的可行性.
- 评估机器学习模型在基于语音特征的情绪状态分类方面的表现.
主要方法:
- 从现实世界的情感日记录音中提取了22个声音特征.
- 采用了一个离开一个主题的交叉验证策略.
- 训练并验证了四种分类模型:中文语音预训练GRU,Gate Recurrent Unit (GRU),双向长期短期记忆 (BiLSTM) 和线性差异分析 (LDA).
主要成果:
- 中国语音预训格鲁模型实现了最高的性能.
- 对于抑郁状态,获得了77.5%的灵敏度和86.1%的特异性.
- 对于躁狂状态,获得了54.8%的灵敏度和90.3%的特异性,整体准确率为80.2%.
结论:
- 机器学习模型可以使用语音分析可靠地区分抑郁和躁狂情绪状态.
- 语音分析为评估情绪障碍提供了一个潜在的客观和精确的方法.
- 这种方法可以提高情绪障碍的临床评估和管理.
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