DPPAT:用于心脏声音声识别的双层周期性模式感知变压器
IEEE journal of biomedical and health informatics
|November 27, 2025
概括
这项研究引入了一种新的双级周期性模式感知变压器 (DPPAT),用于改进心脏声的识别. 该方法通过有效分析心脏声信号,增强了早期心脏病查.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 早期心脏病查依赖于准确的心脏声音分类来识别声.
- 鉴别声是具有挑战性的,因为信号弱和噪声干扰.
- 目前的方法未充分利用内在心脏声音的周期性模式.
研究的目的:
- 提出一种新的双级周期性模式识别变压器 (DPPAT) 进行增强的声识别.
- 在不需要循环分割的情况下,隐式地利用心脏声音信号中的周期性先验.
- 为了提高心脏声检测算法的准确性和通用性.
主要方法:
- 开发了一种双层周期模式识别变压器 (DPPAT) 模型.
- 实施了适应期对准窗口选择算法,用于区域级特征提取.
- 使用周期性模式注意模块来抑制噪音和提取周期性组件.
- 全球层面的综合周期性特征,用于增强语歧视性特征识别.
主要成果:
- 在2022年乔治·B·穆迪物理网络挑战数据集上获得了84.27%的加权准确率和70.38%的F1得分.
- 在包含心脏和呼吸系统声音的两个额外的公共数据集上证明了概括性.
- 注意力可视化证实了模型的焦点和决策依据,用于声识别.
结论:
- DPPAT模型有效地利用周期性先验来改善心脏声的识别.
- 拟议的方法在自动心脏听力和早期疾病查方面提供了有前途的进展.
- DPPAT在不同数据集中显示出强大的性能和通用性,突出显示了其临床潜力.
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