SLPM:一种轻量级的深度学习模型,用于端到端的纸张ECG数字化
Xiankai Yu1, Jian Wu1, Jiahao Wang1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai, 200093, CHINA.
Physiological measurement
|January 30, 2026
概括
我们开发了一个信号位置预测模型 (SLPM) 来准确地数字化纸张心电图 (ECG). SLPM克服了噪音和泛化问题,提供了精确有效的ECG信号提取方法.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 纸张心电图 (ECG) 的数字化是具有挑战性的,因为错误,噪音,和糟糕的概括.
- 现有的方法难以应对复杂的条件和精确的信号提取.
研究的目的:
- 提出一个端到端的信号位置预测模型 (SLPM),用于高保真性纸张心电图的数字化.
- 解决ECG信号细分,噪声干扰和概括方面的挑战.
主要方法:
- SLPM使用一个联合分类回归框架,用于精确的ECG图像到时间序列映射.
- 一个层次化的挤压和激发双向长期短期记忆 (SE-BiLSTM) 增强了特征表示和时间依赖.
主要成果:
- 在单线数据集 (PCC=0.97,SNR≈13.64 dB) 上,SLPM甚至在扭曲的情况下也实现了高精度.
- 在一个12个数据集上,SLPM仅用0.31万个参数达到14.66dB的SNR.
结论:
- 在纸张心电图数字化方面,SLPM在准确性,效率和概括性方面具有显著的优势.
- 该模型代表了对历史ECG数据的高保真数字化有前途的进步.
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