用神经网络进行心跳音频信号分析,用于诊断心血管异常
Vibha Jain1, Ishwari Singh Rajput2, Aditya Gupta3
1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
Health information science and systems
|December 16, 2025
概括
这项研究引入了一种新的AI模型,可以准确地分类心跳信号,有助于早期发现心血管疾病. 先进的神经网络实现了高精度,为改进的诊断工具铺平了道路.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,需要早期检测和管理.
- 准确分析心跳信号对于识别心血管疾病和改善患者的治疗结果至关重要.
- 及时预测心血管疾病对于及时干预和更好的患者结果至关重要.
研究的目的:
- 为自动心跳信号分类开发一种弹性神经网络架构.
- 将卷积神经网络 (CNN) 与长期短期记忆 (LSTM) 网络集成,以提高诊断能力.
- 评估模型在用于心血管疾病检测的公开数据集上的表现.
主要方法:
- 实现了一个混合CNN-LSTM神经网络架构.
- 应用全面的数据预处理技术,包括平衡,规范化和增强.
- 利用特征提取来提高心跳信号质量进行分析.
主要成果:
- 在对心跳信号的二进制分类中达到高达96.04%的准确性.
- 在三类分类任务中达到97.83%的准确性.
- 与最近的基线模型相比,在类似的数据集上表现出竞争性表现.
结论:
- 拟议的基于音频的诊断框架显示了自动心脏声音分类的巨大潜力.
- 将听觉信号纳入决策支持系统是可行的和有益的.
- 未来的工作将集中在模型增强,数据集增强和探索额外的预测变量以增加临床相关性.
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