Real-Time Fast Fourier Transform-Based Notch Filter for Single-Frequency Noise Cancellation: Application to Electrocardiogram Signal Denoising.

IF 1.1 Q4 ENGINEERING, BIOMEDICAL
Journal of Medical Signals & Sensors Pub Date : 2021-01-30 eCollection Date: 2021-01-01 DOI:10.4103/jmss.JMSS_3_20
Anis Ben Slimane, Azza Ouled Zaid
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引用次数: 4

Abstract

Despite the considerable improvement of the common-mode rejection ratio of digital filtering techniques, the electrocardiogram (ECG) traces recorded by commercialized devices are still contaminated by residual power line interference (PLI). In this study, we address this issue by proposing a novel real-time filter adapted to single-frequency noise cancellation and automatic power line frequency detection. The filtering process is principally based on a point-by-point fast Fourier transform and a judicious choice of the analysis window length. Intensive experiments conducted on real and synthetic signals have shown that our filtering method offers very clean ECGs, due to the suppression of spikes corresponding to the PLI and the preservation of spikes outside the filter band. In addition, this method is characterized by its low computational complexity which makes it suitable for real-time cleaning of ECG signals and thus can serve for more accurate diagnosis in computer-based automated cardiac system.

Abstract Image

Abstract Image

Abstract Image

基于实时快速傅立叶变换的单频降噪陷波滤波器:在心电图信号降噪中的应用。
尽管数字滤波技术的共模抑制比有了很大的提高,但商业化设备记录的心电图(ECG)迹线仍然受到残余电力线干扰(PLI)的污染。在这项研究中,我们提出了一种适用于单频噪声消除和自动电力线频率检测的新型实时滤波器来解决这个问题。滤波过程主要基于逐点快速傅里叶变换和分析窗口长度的明智选择。在真实信号和合成信号上进行的大量实验表明,我们的滤波方法提供了非常干净的心电图,这是由于抑制了与PLI对应的尖峰,并保留了滤波器带外的尖峰。此外,该方法计算复杂度低,适用于心电信号的实时清洗,可为基于计算机的自动化心脏系统提供更准确的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Medical Signals & Sensors
Journal of Medical Signals & Sensors ENGINEERING, BIOMEDICAL-
CiteScore
2.30
自引率
0.00%
发文量
53
审稿时长
33 weeks
期刊介绍: JMSS is an interdisciplinary journal that incorporates all aspects of the biomedical engineering including bioelectrics, bioinformatics, medical physics, health technology assessment, etc. Subject areas covered by the journal include: - Bioelectric: Bioinstruments Biosensors Modeling Biomedical signal processing Medical image analysis and processing Medical imaging devices Control of biological systems Neuromuscular systems Cognitive sciences Telemedicine Robotic Medical ultrasonography Bioelectromagnetics Electrophysiology Cell tracking - Bioinformatics and medical informatics: Analysis of biological data Data mining Stochastic modeling Computational genomics Artificial intelligence & fuzzy Applications Medical softwares Bioalgorithms Electronic health - Biophysics and medical physics: Computed tomography Radiation therapy Laser therapy - Education in biomedical engineering - Health technology assessment - Standard in biomedical engineering.
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