An error-bounded median filter for correcting ECG baseline wander.

IF 4.7 3区 医学 Q1 MEDICAL INFORMATICS
Health Information Science and Systems Pub Date : 2023-09-26 eCollection Date: 2023-12-01 DOI:10.1007/s13755-023-00235-w
Huanyu Zhao, Tongliang Li, Jian Yang, Chaoyi Pang
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引用次数: 0

Abstract

The baseline wander (BLW) in electrocardiogram (ECG) is a common disturbance that has a significant influence on the ECG wave pattern recognition. Many methods, such as IIR filter, mean filter, etc., can be used to correct BLW; However, most of them work on the original ECG signals. Compressed ECG data are economic for data storage and transmission, and if the baseline correction can be processed on them, it will be more efficient than we decompress them first and then do such correction. In this paper, we propose a new type of median filter CM_Filter, which works on the synopses of straight lines achieved from ECG by piecewise linear approximation (PLA) under maximum error bound. In CM_Filter, a heuristic strategy "Quick-Finding" is deduced by a property of straight lines in order to get the quality-assured median values from the synopses. The extended experimental tests demonstrate that the proposed filter is very efficient in execution time, and effective for correcting both slow and abrupt ECG baseline wander.

一种用于校正ECG基线漂移的误差有界中值滤波器。
心电图中的基线漂移(BLW)是一种常见的干扰,对心电波形识别有重要影响。可以使用许多方法,如IIR滤波器、均值滤波器等来校正BLW;然而,它们中的大多数对原始ECG信号进行处理。压缩的ECG数据对于数据存储和传输来说是经济的,如果可以对它们进行基线校正,那么它将比我们首先对它们进行解压缩然后进行这样的校正更有效。在本文中,我们提出了一种新型的中值滤波器CM_filter,它适用于在最大误差范围下通过分段线性近似(PLA)从ECG获得的直线的概图。在CM_Filter中,利用直线的性质推导了一种启发式策略“快速查找”,以从摘要中获得质量保证的中值。扩展的实验测试表明,所提出的滤波器在执行时间上非常有效,并且对于校正缓慢和突然的ECG基线漂移都是有效的。
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来源期刊
CiteScore
11.30
自引率
5.00%
发文量
30
期刊介绍: Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.
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