卷积变压器驱动的强大的心电图信号无声化框架,具有适应性参数ReLU

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Jing Wang¹, Shicheng Pei², Yihang Yang¹

¹School of Computer Science, Xi'an Polytechnic University, Xi'an 710021, China.

Mathematical biosciences and engineering : MBE

|March 29, 2024

概括

这项研究介绍了APtrans-CNN,这是一种用于心电图 (ECG) 信号消噪的新型深度学习模型. 该框架有效消除噪音,显著提高心血管疾病的诊断准确度.

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
信号处理信号处理

背景情况:

电心电图 (ECG) 对于诊断心血管疾病至关重要.
电脑心电图信号容易受到噪音的影响,这阻碍了准确的临床评估.
现有的无声化方法可能会与复杂的噪声模式和长时间序列特征作斗争.

研究的目的:

开发一个先进的深度学习框架,以有效地消除ECG信号.
通过减少噪音干扰来提高心电图记录的临床实用性.
通过更清洁的心电图数据,提高心血管疾病诊断的准确性.

主要方法:

提出了一个新的基于变压器的卷积神经网络 (APtrans-CNN) 框架.
集成变压器用于全球特征学习,CNN用于本地特征学习.
引入了自适应参数ReLU (APReLU) 和一个动态特征聚合模块.

主要成果:

APtrans-CNN 准确地从杂的数据集中提取纯电图信号.
信号与噪声比 (SNR) 显著改善,从-4dB提高到6dB以上.
在无声化后,诊断模型的准确性超过96%

结论:

APtrans-CNN框架提供了有效的ECG信号拒绝能力.
该模型学习全球和本地特征的能力提高了其在复杂的时间序列数据上的性能.
APtrans-CNN显示了适应各种应用的适应性,改善了心血管诊断.

关键词:

这是一个ECG信号.卷积神经网络是一种卷积神经网络.信号无声化信号无声化变压器的变压器是一个变压器.

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