看看你的言语:使用卷积神经网络识别精神病的诊断状态和负面症状
Gleb Melshin1,2, Anthony DiMaggio1,3, Nadia Zeramdini1
1Douglas Research Centre, McGill University, Montreal, QC Canada.
NPP - digital psychiatry and neuroscience
|July 11, 2025
概括
使用卷积神经网络 (CNN) 分析的语音谱图可以准确检测精神分裂症谱系障碍并评估负面症状的严重程度. 这种方法捕捉了动态的声音模式,为临床监测提供了一个新的工具.
科学领域:
- 计算精神病学是一种计算精神病学.
- 语音信号处理 语音信号处理
- 机器学习在心理健康中的应用
背景情况:
- 需要对精神分裂症的客观生物标志物来监测症状负担.
- 静态声学特征在捕捉动态语音细微差别方面存在局限性.
- 临床医生在面试期间自然会评估时间变化的声音线索,比如负面症状.
研究的目的:
- 调查语音谱图和CNN用于诊断精神分裂症谱系障碍的潜力.
- 评估该方法在确定负面症状严重程度和模糊影响方面的能力.
- 使用Grad-CAM可视化探索基于CNN的分类的可解释性.
主要方法:
- 使用了319名参与者的语音录音 (227名精神分裂症患者,92名对照患者).
- 音频被分割成10秒的片段,并转化为日志-Mel光谱图.
- 修改后的ResNet-18CNN执行了分类任务:诊断,负面症状负担和形影响.
主要成果:
- CNNs将精神分裂症频谱参与者与对照者区分开来,准确度为87.8% (AUC=0.86).
- 对负症状负荷的模型达到80.5%的准确率 (AUC=0.73),而对于形影响的准确率为87.8% (AUC=0.79).
- 格拉德-CAM可视化显示,CNN专注于临床相关的发音模式在发言层面.
结论:
- 基于频谱的CNN对短语段的分析可可靠地检测出精神分裂症谱系障碍.
- 这种方法有效地确定了负面症状的负担和模糊的情绪.
- 可解释的框架强调了时频语音特征对于细微的症状跟踪的有用性.
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