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一种使用样本增强和INDANet进行心声分类的方法
Jinpo Wang1, Zijian Qiao1,2, Yudong Yao3
1Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, Zhejiang, China.
The Review of scientific instruments
|February 5, 2026
概括
心血管疾病的诊断通过人工智能得到了改进. 一种新的方法,INDANet,提高了心声分类的准确性,特别是在临床经验有限的地区.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,不成比例地影响欠发达地区.
- 在资源有限的环境中,通过听觉诊断心脏病的临床专业知识有限,需要先进的诊断工具.
- 现有的人工智能 (AI) 方法对心脏声音分类面临挑战,因为数据集很小,噪声水平很高,影响准确性.
研究的目的:
- 开发和评估一种基于人工智能的新方法,用于准确地分类心脏声音,以帮助诊断心血管疾病.
- 为了解决小样本大小和心脏声音数据中显著噪声的局限性.
- 提高人工智能模型对辅助心脏诊断的稳定性和概括能力.
主要方法:
- 预处理心脏声音,使用Butterworth波器去除外来噪音.
- 实施样本增量技术以扩大培训数据集.
- 开发注入噪声双重注意网络 (INDANet),将道和空间注意机制与注入高斯噪声相结合,以提高稳定性.
主要成果:
- 拟议的INDANet方法在心脏声音分类任务中,与其他六种先进模型相比,表现优越.
- 在一个数据集上达到99.85%的高准确率,在另一个数据集上达到98.07%.
- 样本增大和注入噪声的整合显著提高了模型的稳定性和通用性.
结论:
- INDANet方法提供了一个有希望的AI驱动解决方案,用于准确的心脏声音分类,特别有利于资源有限的地区的临床医生.
- 双重注意力机制与数据增强和噪声注入相结合,有效地提高了心血管疾病的诊断准确性.
- 这种方法有可能在全球范围内显著改善心血管疾病的早期检测和管理.
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