MrSeNet:基于多分辨率残留注意网络的心电图信号消噪
Zhen Wang1, Hanshuang Xie1, Yamin Liu1
1Research and Development Department, Hangzhou Proton Technology Co., Ltd, Hangzhou 310012, China.
Journal of electrocardiology
|January 1, 2025
概括
本研究介绍了MrSeNet,这是一种用于减少心电图 (ECG) 信号中的噪声的深度学习模型. 这种新型网络有效地提高了心电图数据的质量,以改善心脏病诊断.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 医疗保健中的人工智能
背景情况:
- 电心电图 (ECG) 对于诊断心脏疾病至关重要,但易受噪声影响.
- 信号噪声降低了心电图质量,可能导致误诊.
- 有效的心电图无声化对于准确的临床解释至关重要.
研究的目的:
- 开发一种先进的深度学习模型,用于强大的心电图信号消噪.
- 提高基于心电图的心脏诊断的准确性和可靠性.
主要方法:
- 提出了一个具有注意力机制的端到端多解析度深度学习网络 (MrSeNet).
- 使用挤压激发模块来增强心电图信号功能.
- 在CPSC2018和MIT-BIH数据库中对模型进行了验证,并添加了噪声 (NSTDB).
主要成果:
- 与传统的消毒方法相比,MrSeNet表现出更高的性能.
- 该模型在各种噪声强度下实现了显著的降噪.
- 使用皮尔森相关系数,SNR和RMSE来评估性能.
结论:
- MrSeNet有效地拒绝了心电图信号,保存了诊断信息.
- 拟议的深度学习方法在临床实践中显示出有前途的未来应用.
- 增强的心电图信号质量支持更准确的心脏病状况检测和管理.
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