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首页> 外文期刊>Physical and Engineering Sciences in Medicine >Basis pursuit sparse decomposition using tunable?Q wavelet transform (BPSD?TQWT) for denoising of electrocardiograms
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Basis pursuit sparse decomposition using tunable?Q wavelet transform (BPSD?TQWT) for denoising of electrocardiograms

机译：基础追求稀疏分解使用可调？Q小波变换（BPSD？TQWT） 用于心电图去噪

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The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in anECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI)and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD)using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly,the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural nonstationarybehaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Qualityfactor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decompositionlevels (J2). The ‘r’ and ‘J’ values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR).It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levelsof 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed methodBPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collectedthrough a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performancewas then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlationcoefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM)and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluationparameters such as SNR, MAX, and NCC were improved by 4.3 dB and 6.8 dB, 0.37 and 0.78, 0.2 and 0.46 compared toIIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR,MAX, and NCC were improved by 0.3 dB and 0.6 dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methodsrespectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM andLM methods for PLI and BD removal.
机译：心电图(ECG)是至关重要的诊断工具来识别心脏异常。采集器是噪声干扰。本质上,著名的心电图噪声源电力线路干扰(PLI)(BD)。叫做追求稀疏分解的基础(BPSD)提出了消除PLI和BD出现在心电图记录。小波变换不同的质量因素(问),自然可以调整信号非平稳的此外,信号分解的方法高质量的因素和低质量小波系数消除的组成部分照明灯具和BD通过选择适当的冗余(右)和分解值选择基于试错方法对信噪比(信噪比)。已经发现PLI噪音吗显著抑制冗余的3和分解水平和4的冗余和被移除吗分解层次的19所示。方法开源MIT-BIH心律失常数据库和实时心电图记录收集可穿戴的镀银的尼龙编织(Ag-NyW)textile-based心电图监测系统。表演信噪比等指标,最大绝对误差(MAX)和规范化互相关是与IIR滤波器相比,固定小波变换(SWT),非本地意味着(NLM)意味着(LM)方法。MIT-BIH心律失常数据库,性能评价提高了4.3 dB和6.8 dB, 0.37和0.78、0.2和0.46相比方法分别。该方法在实时数据集,信噪比的值,dB和0.6、0.009和0.74,0.3和0.35而信息检索和SWT方法最后,它可以得出结论,提出方法显示在检索性能改善,SWT,NLM和

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