{"title":"基于增强变模分解的肺部心音噪声分离技术用于肺部疾病诊断","authors":"B. Sangeetha, R. Periyasamy","doi":"10.4015/s1016237223500357","DOIUrl":null,"url":null,"abstract":"Lung sound (LS) signals are vital for diagnosing pulmonary disorders. However, heart sound (HS) interferes with the analysis of LS, leading to the misdiagnosis of lung disorders. To address this issue, we propose an Enhanced Variational Mode Decomposition (E-VMD) technique to remove HS interference from LSs effectively. The E-VMD method automatically determines the mode number for signal decomposition based on the characteristics of variational mode functions (VMFs) such as normalized permutation entropy, kurtosis index, extreme frequency domain, and energy loss coefficient. The performance of the proposed denoising technique was evaluated using six performance metrics: Signal-to-noise ratio (SNR), root mean square error (RMSE), normalized mean absolute error (nMAE), correlation coefficient factor (CCF), CPU[Formula: see text], and CPU[Formula: see text]. In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. This technique utilizes an electronic stethoscope, a common clinical device used by healthcare professionals for detecting lung disease.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HEART SOUND NOISE SEPARATION FROM LUNG SOUND BASED ON ENHANCED VARIATIONAL MODE DECOMPOSITION FOR DIAGNOSING PULMONARY DISEASES\",\"authors\":\"B. Sangeetha, R. 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In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. 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引用次数: 0
摘要
肺音(LS)信号对诊断肺部疾病至关重要。然而,心音(HS)会干扰 LS 的分析,导致肺部疾病的误诊。为解决这一问题,我们提出了一种增强变异模式分解(E-VMD)技术,以有效去除 LS 中的 HS 干扰。E-VMD 方法根据变异模态函数(VMF)的归一化排列熵、峰度指数、极频域和能量损失系数等特征,自动确定信号分解的模态数。利用六项性能指标对所提出的去噪技术的性能进行了评估:信噪比(SNR)、均方根误差(RMSE)、归一化平均绝对误差(nMAE)、相关系数因子(CCF)、CPU[计算公式:见正文]和 CPU[计算公式:见正文]。与其他去噪方法(如经验模式分解法(EMD)、集合经验模式分解法(EEMD)、互补集合经验模式分解法(CEEMD)、奇异频谱分析法(SSA)和变异模式分解法(VMD))相比,新的 E-VMD 方法显示出更优越的去噪效果。我们使用坦贾武尔医学院和坦贾武尔医院胸腔内科门诊部记录的 LS 对所提出的方法进行了评估。获得的性能指标如下RMSE:0.02103 ± 0.00054,SNR:28.52464 ± 0.00253,nMAE:0.00009 ± 0.00056,CCF:0.9962,CPU[计算公式:见正文]:34.586,CPU[计算公式:见正文]:0.9962:34.586,CPU[公式:见文本]:0.452 秒:这些结果肯定了所提方法的适应性和鲁棒性,即使在存在 HS 噪声的情况下也是如此。该方法提高了去噪精度和计算效率,使其成为改进 LS 信号分析和辅助医疗诊断的有用工具。这项技术利用了电子听诊器,这是医护人员用于检测肺部疾病的常用临床设备。
HEART SOUND NOISE SEPARATION FROM LUNG SOUND BASED ON ENHANCED VARIATIONAL MODE DECOMPOSITION FOR DIAGNOSING PULMONARY DISEASES
Lung sound (LS) signals are vital for diagnosing pulmonary disorders. However, heart sound (HS) interferes with the analysis of LS, leading to the misdiagnosis of lung disorders. To address this issue, we propose an Enhanced Variational Mode Decomposition (E-VMD) technique to remove HS interference from LSs effectively. The E-VMD method automatically determines the mode number for signal decomposition based on the characteristics of variational mode functions (VMFs) such as normalized permutation entropy, kurtosis index, extreme frequency domain, and energy loss coefficient. The performance of the proposed denoising technique was evaluated using six performance metrics: Signal-to-noise ratio (SNR), root mean square error (RMSE), normalized mean absolute error (nMAE), correlation coefficient factor (CCF), CPU[Formula: see text], and CPU[Formula: see text]. In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. This technique utilizes an electronic stethoscope, a common clinical device used by healthcare professionals for detecting lung disease.
期刊介绍:
Biomedical Engineering: Applications, Basis and Communications is an international, interdisciplinary journal aiming at publishing up-to-date contributions on original clinical and basic research in the biomedical engineering. Research of biomedical engineering has grown tremendously in the past few decades. Meanwhile, several outstanding journals in the field have emerged, with different emphases and objectives. We hope this journal will serve as a new forum for both scientists and clinicians to share their ideas and the results of their studies.
Biomedical Engineering: Applications, Basis and Communications explores all facets of biomedical engineering, with emphasis on both the clinical and scientific aspects of the study. It covers the fields of bioelectronics, biomaterials, biomechanics, bioinformatics, nano-biological sciences and clinical engineering. The journal fulfils this aim by publishing regular research / clinical articles, short communications, technical notes and review papers. Papers from both basic research and clinical investigations will be considered.