一个新的自适应CNN-LSTM融合网络用于心电图诊断
Yuxuan Wu1, Jijun Tong2, Pengjia Qi2
1School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
Physiological measurement
|December 19, 2025
概括
这项研究引入了适应性CNN-LSTM网络,用于改善心电图 (ECG) 分析,用于诊断心血管疾病 (CVD). 这种新的方法提高了早期心血管疾病查的诊断准确性和效率.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心血管疾病 (CVD) 构成了全球重大健康风险.
- 电心电图 (ECG) 对于早期发现心血管疾病至关重要.
- 目前的深度学习模型,如CNN和LSTM在心电图分析方面存在局限性.
研究的目的:
- 提出一种适应性的CNN-LSTM (aCNN-LSTM) 融合网络,以改善心电图诊断.
- 解决CNN中固定内核的局限性和LSTM中局部特征相关性问题.
- 提高基于心电图的自动心血管疾病查的效率和准确性.
主要方法:
- 开发了一个自适应卷积内核,可以根据信号方差动态调整大小.
- 将自适应卷积特征集成到LSTM网络中,以捕捉时间关系.
- 采用时空融合机制来对心电图数据进行多类分类.
主要成果:
- 与标准的CNN,LSTM和CNN-LSTM模型相比,aCNN-LSTM网络在PTB-XL数据集上取得了卓越的性能.
- 实现了总体准确度为89.89%.
- 获得的宏观平均F1得分为0.9640和加权平均F1得分为0.9698.
结论:
- 拟议的aCNN-LSTM网络显著提高了自动心电图诊断的效率和准确性.
- 该方法为临床和初级保健机构的早期心血管疾病查提供了可靠的技术支持.
- 适应性内核设计提高了用于复杂生物医学信号分析的深度学习模型性能.
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