| 陈波,孙辉,储昭碧,李育玲,魏嘉乐.面向ECG彩虹码的双输入改进VIT识别研究[J].电子测量与仪器学报,2024,38(11):200-209 |
| 面向ECG彩虹码的双输入改进VIT识别研究 |
| Research on two-input improved VIT recognition for ECG rainbow codes |
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| DOI： |
| 中文关键词: 心电信号 ECG彩虹码 图像变换 双输入特征提取模块 改进VIT |
| 英文关键词:electrocardiography ECG rainbow code image transformation dual input feature extraction module improved VIT |
| 基金项目: |
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| Author | Institution |
| Chen Bo | 1.Anhui Province Key Laboratory of Semiconductor Packaging and Reliability (Hefei University of Technology),
Hefei 230009, China; 2.School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Sun Hui | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Chu Zhaobi | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Li Yuling | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
| Wei Jiale | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
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| 摘要点击次数: 315 |
| 全文下载次数: 1176 |
| 中文摘要: |
| 基于海量ECG数据,辅助医生进行有效数据分析与诊断，提高效率并减少医疗资源消耗,实现ECG智能识别是当前一个重要研究方向。针对ECG智能识别单一图像、单一深度学习算法性能有限性问题,提出了一种面向ECG彩虹码的双输入改进VIT识别方法。首先,提出数学模型预测获取ECG标准周期,并以抽频方法挖掘ECG潜在特征,生成ECG彩虹码;然后,以卷积神经网络构建双输入特征提取模块,提取多种ECG图像局部特征进行融合,实现多维度ECG特征表示与融合,采用VIT编码模块对融合特征进行全局关注,实现基于多特征图像为输入的ECG识别。采用MIT-BIH数据库中的ECG进行实验,所提ECG识别方法获得99.41%的平均准确率,在现场采集的N类ECG中获得100%的准确率。实验结果表明,提出的图像变换方法能够有效可视化ECG特征，提出的识别方法能够有效实现ECG识别,与其他同类型方法相比获得了更优的性能。 |
| 英文摘要: |
| Leveraging extensive ECG data, intelligent ECG recognition represents a pivotal research focus aimed at supporting physicians in conducting thorough data analysis and diagnosis, thereby enhancing efficiency and mitigating medical resource consumption. In order to solve the problem of feature loss and limited performance of single image and single deep learning algorithm in ECG intelligent recognition, a two-input improved VIT recognition method for ECG rainbow code is proposed. Firstly, a mathematical model is proposed to predict the standard period of ECG, and the potential features of ECG are mined by pumping method to generate ECG rainbow code. Then, a dual input feature extraction module is constructed with convolutional neural network to extract local features of multiple ECG images for fusion to achieve multi-dimensional ECG feature representation and fusion. A VIT coding module is used to pay global attention to fusion features to realize ECG recognition based on multi-feature images as input. The ECG recognition method in MIT-BIH database is used for experiments, and the average accuracy of the proposed ECG recognition method is 99.41%, and the accuracy of the N-type ECG collected in the field is 100%. The experimental results show that the proposed image transformation method can effectively visualize ECG features, and the effect is better than the traditional method. The proposed recognition method can realize ECG recognition effectively and has better performance than other similar methods. |
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