Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder

计算机科学 自编码 人工智能 模式识别(心理学) 编码器 降噪 假阳性悖论 噪音(视频) 语音识别 条纹 深度学习 操作系统 图像(数学) 物理 光学
作者
Meng Chen,Yongjian Li,Liting Zhang,Lei Liu,Baokun Han,Wenzhuo Shi,Shoushui Wei
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (4): 1993-2004 被引量:10
标识
DOI:10.1109/jbhi.2024.3355960
摘要

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
光能使者发布了新的文献求助10
1秒前
昭荃完成签到 ,获得积分0
1秒前
xiaoyy完成签到,获得积分10
1秒前
开朗艳一发布了新的文献求助10
1秒前
怪咖完成签到,获得积分10
2秒前
朝颜完成签到,获得积分10
2秒前
2秒前
3秒前
天天快乐应助邸泽阳采纳,获得10
3秒前
weijian完成签到,获得积分10
3秒前
qin希望应助小坤同学采纳,获得10
3秒前
恋雅颖月发布了新的文献求助20
4秒前
xide完成签到,获得积分10
5秒前
张瑞宁发布了新的文献求助10
5秒前
Owen应助梁晓雯采纳,获得10
6秒前
7秒前
怪咖发布了新的文献求助10
8秒前
Pooh完成签到,获得积分10
8秒前
8秒前
Yun发布了新的文献求助10
8秒前
8秒前
高兴金毛完成签到,获得积分20
8秒前
9秒前
祁i关注了科研通微信公众号
10秒前
顾矜应助缥缈易槐采纳,获得10
12秒前
markowits发布了新的文献求助10
12秒前
13秒前
13秒前
高兴金毛发布了新的文献求助10
13秒前
14秒前
过时的笙完成签到,获得积分10
16秒前
16秒前
19秒前
20秒前
丘比特应助沐晴采纳,获得10
20秒前
xiaoyy发布了新的文献求助10
20秒前
xqxqxqxqxqx完成签到,获得积分10
21秒前
22秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988827
求助须知:如何正确求助?哪些是违规求助? 3531183
关于积分的说明 11252671
捐赠科研通 3269809
什么是DOI,文献DOI怎么找? 1804780
邀请新用户注册赠送积分活动 881885
科研通“疑难数据库(出版商)”最低求助积分说明 809021