{"title":"基于经验模态分解和混合滤波器融合的多准则贝叶斯优化增强心电信号去噪与分类:心律失常与心肌梗死病例。","authors":"Dounia Bentaleb, Zakaria Khatar","doi":"10.1016/j.compbiomed.2024.109462","DOIUrl":null,"url":null,"abstract":"<p><p>This paper introduces a new advanced model for denoising and classification of ECG signals, focusing on the use of a hybrid filter and Bayesian optimization. The hybrid filter synergistically combines enhanced empirical mode decomposition (EEMD), Chebyshev Type II filters, Butterworth, Daubechies Wavelet, and Savitzky-Golay filters, leveraging their respective advantages for effective noise reduction while preserving the essential features of the ECG signal. We employ a multi-criteria Bayesian optimization process, using cross-correlation and mean squared error (MSE) as key metrics, to refine the filter parameters to further improve the signal quality. Additionally, we deploy a residual and parallel deep learning architecture adapted for the precise classification of cardiac conditions, such as arrhythmia and myocardial infarction. The results show a significant improvement in classification accuracy, from 96.63%-97.86% to 97.24%-98.62% after filtering, and reaching 98.03%-99.61% after optimization for arrhythmia. For myocardial infarction, accuracy increases from 96.29%-97.42% to 97.34%-98.44% after filtering, and from 98.13%-99.07% after optimization. This holistic and multi-criteria approach enhances the reliability of medical diagnostics and represents a significant advancement in the processing of biomedical signals.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"109462"},"PeriodicalIF":7.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-criteria Bayesian optimization of Empirical Mode Decomposition and hybrid filters fusion for enhanced ECG signal denoising and classification: Cardiac arrhythmia and myocardial infarction cases.\",\"authors\":\"Dounia Bentaleb, Zakaria Khatar\",\"doi\":\"10.1016/j.compbiomed.2024.109462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper introduces a new advanced model for denoising and classification of ECG signals, focusing on the use of a hybrid filter and Bayesian optimization. The hybrid filter synergistically combines enhanced empirical mode decomposition (EEMD), Chebyshev Type II filters, Butterworth, Daubechies Wavelet, and Savitzky-Golay filters, leveraging their respective advantages for effective noise reduction while preserving the essential features of the ECG signal. We employ a multi-criteria Bayesian optimization process, using cross-correlation and mean squared error (MSE) as key metrics, to refine the filter parameters to further improve the signal quality. Additionally, we deploy a residual and parallel deep learning architecture adapted for the precise classification of cardiac conditions, such as arrhythmia and myocardial infarction. The results show a significant improvement in classification accuracy, from 96.63%-97.86% to 97.24%-98.62% after filtering, and reaching 98.03%-99.61% after optimization for arrhythmia. For myocardial infarction, accuracy increases from 96.29%-97.42% to 97.34%-98.44% after filtering, and from 98.13%-99.07% after optimization. This holistic and multi-criteria approach enhances the reliability of medical diagnostics and represents a significant advancement in the processing of biomedical signals.</p>\",\"PeriodicalId\":10578,\"journal\":{\"name\":\"Computers in biology and medicine\",\"volume\":\"184 \",\"pages\":\"109462\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in biology and medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1016/j.compbiomed.2024.109462\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.compbiomed.2024.109462","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/29 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
本文介绍了一种新的先进的心电信号去噪和分类模型,重点介绍了混合滤波器和贝叶斯优化的使用。该混合滤波器将增强经验模态分解(EEMD)、Chebyshev Type II滤波器、Butterworth滤波器、Daubechies小波滤波器和Savitzky-Golay滤波器协同结合,在保留心电信号基本特征的同时,利用各自的优势进行有效的降噪。我们采用多准则贝叶斯优化过程,以相互关系和均方误差(MSE)为关键指标,来细化滤波器参数,以进一步提高信号质量。此外,我们部署了一个残差并行深度学习架构,适用于心律失常和心肌梗死等心脏状况的精确分类。结果表明,过滤后的分类准确率从96.63% ~ 97.86%提高到97.24% ~ 98.62%,优化后的心律失常分类准确率达到98.03% ~ 99.61%。对于心肌梗死,过滤后的准确率从96.29%-97.42%提高到97.34%-98.44%,优化后的准确率从98.13%-99.07%提高到98.13%-99.07%。这种整体和多标准的方法提高了医学诊断的可靠性,代表了生物医学信号处理的重大进步。
Multi-criteria Bayesian optimization of Empirical Mode Decomposition and hybrid filters fusion for enhanced ECG signal denoising and classification: Cardiac arrhythmia and myocardial infarction cases.
This paper introduces a new advanced model for denoising and classification of ECG signals, focusing on the use of a hybrid filter and Bayesian optimization. The hybrid filter synergistically combines enhanced empirical mode decomposition (EEMD), Chebyshev Type II filters, Butterworth, Daubechies Wavelet, and Savitzky-Golay filters, leveraging their respective advantages for effective noise reduction while preserving the essential features of the ECG signal. We employ a multi-criteria Bayesian optimization process, using cross-correlation and mean squared error (MSE) as key metrics, to refine the filter parameters to further improve the signal quality. Additionally, we deploy a residual and parallel deep learning architecture adapted for the precise classification of cardiac conditions, such as arrhythmia and myocardial infarction. The results show a significant improvement in classification accuracy, from 96.63%-97.86% to 97.24%-98.62% after filtering, and reaching 98.03%-99.61% after optimization for arrhythmia. For myocardial infarction, accuracy increases from 96.29%-97.42% to 97.34%-98.44% after filtering, and from 98.13%-99.07% after optimization. This holistic and multi-criteria approach enhances the reliability of medical diagnostics and represents a significant advancement in the processing of biomedical signals.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.