基于改进的ConvNeXt模型,对患有脑血管狭窄症的患者进行打声音分类
Caijian Hua1, Zhihui Liu1, Liuying Li2
1School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin, China.
Frontiers in physiology
|December 12, 2025
概括
这项研究引入了一种使用动态卷积和注意力机制的新型打分类方法. 该方法准确地区分正常和异常的打,识别与阻塞性睡眠呼吸暂停患者脑血管狭窄相关的声学标记.
科学领域:
- 医疗声学 医学声学
- 医疗保健中的人工智能
- 神经学 神经学
背景情况:
- 打是阻塞性睡眠呼吸暂停 (OSA) 的关键症状,与脑血管疾病风险有关.
- 现有的探测研究主要集中在没有脑血管疾病的阻塞性睡眠呼吸暂停-呼吸暂停综合征 (OSAHS) 患者身上.
- 需要了解脑血管狭窄症患者和没有脑血管狭窄症患者之间打的声学差异.
研究的目的:
- 提出使用动态卷积和注意力机制的先进打分类方法.
- 为了研究脑血管狭窄症患者和没有脑血管狭窄症患者之间打声音的声学特征变化.
- 为了提高 OSAHS 患者的打检测和分类的准确性.
主要方法:
- 收集了31名OSAHS患者 (16名脑血管狭窄症患者) 的打声音.
- 提取的声学特征包括Mel光谱图,MFCC,CQT和CENS.
- 开发了一个基于ConvNeXt的分类模型,并使用AKConv,CBAM和Conv2Former模块进行了增强.
- 进行了打与非打的分类以及静脉与非静脉打的分类,并进行了废除研究和曼-惠特尼U测试.
主要成果:
- 拟议的方法在Mel光谱图上的打分类中获得了90.24%的准确性,超过了基线ConvNeXt (88.16%).
- 该模型在分类狭窄性与非狭窄性打方面表现出卓越的表现.
- 废弃性研究证实了所有添加的模块对性能增强的贡献.
- 在狭窄和非狭窄组之间的低频能量比率和打频率中发现了显著的差异 (p < 0.05).
结论:
- 开发的方法在打分类方面表现出高效率.
- 这项研究提供了初步证据,证明了在患有或没有脑血管狭窄症的患者中区分打的声学特征.
- 这项研究为诊断和管理患有脑血管疾病的OSAHS患者的非侵入性声学分析开辟了道路.
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