声音如钟:通过语音声学生物标志物进行健康状况分类的深度学习方法
Yanbing Wang1, Haiyan Wang1, Zhuoxuan Li1
1School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
Chinese medicine
|July 25, 2024
概括
这项研究使用深度学习模型来分析语音音频,成功地识别了身体结构不平衡的个体,这表明了亚健康. 这些发现支持个性化医学的非侵入性健康状况分类.
科学领域:
- 综合医学是一个整体的医学.
- 计算健康科学 计算健康科学
背景情况:
- 人类健康是一个动态的状态,受到多种因素的影响.
- 中国传统医学将健康分为九种身体结构类型.
- 机器学习为各种疾病提供了非侵入性诊断潜力.
研究的目的:
- 利用语音音频数据来识别亚健康人群.
- 根据传统中医的身体构成类型来对个人进行分类.
主要方法:
- 收集了18-45岁的参与者的音频录音.
- 预处理的音频数据转化为Mel频率切普斯特尔系数 (MFCC).
- 实施并比较一维卷积网络 (Conv1D),二维卷积网络 (Conv2D) 和长短期记忆 (LSTM) 深度学习模型进行分类.
主要成果:
- 所有模型在区分平衡 (健康) 和不平衡 (次健康) 构造类型方面都表现出强的表现.
- Conv2D模型获得了最高的曲线下面积 (AUC) 评分0.99.
- 验证准确度在84.19% (Conv1D) 到87.13% (LSTM) 之间.
结论:
- 深度学习模型有效地使用基于身体构成类型的语音音频来分类健康状况.
- 该研究将宪法理论与深度学习相结合,用于非侵入性亚健康识别.
- 这些发现支持个性化医疗和早期干预策略.
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