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Zhu F, Ding J, Li X, Lu Y, Liu X, Jiang F, Zhao Q, Su H, Shuai J. MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis. Health Inf Sci Syst 2024; 12:8. [PMID: 38274493 PMCID: PMC10805910 DOI: 10.1007/s13755-023-00268-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise (SNR) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases.
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Affiliation(s)
- Fangfang Zhu
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005 China
- National Institute for Data Science in Health and Medicine, and State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, 361005 China
| | - Ji Ding
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing, 314006 China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005 China
| | - Yuer Lu
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001 China
| | - Xiao Liu
- School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC Australia
| | - Frank Jiang
- School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC Australia
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051 China
| | - Honghong Su
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing, 314006 China
| | - Jianwei Shuai
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001 China
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Ding W, Wang QG, Zhang JX. Analysis and prediction of COVID-19 epidemic in South Africa. ISA Trans 2022; 124:182-190. [PMID: 33551132 PMCID: PMC7842146 DOI: 10.1016/j.isatra.2021.01.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 12/01/2020] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.
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Affiliation(s)
- Wei Ding
- Faculty of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, PR China; Institute for Intelligent Systems, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Qing-Guo Wang
- Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai; BNU-HKBU United International College, Zhuhai, 519000, PR China.
| | - Jin-Xi Zhang
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, PR China
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Li R, Dudek SM, Kim D, Hall MA, Bradford Y, Peissig PL, Brilliant MH, Linneman JG, McCarty CA, Bao L, Ritchie MD. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network. BioData Min 2016; 9:18. [PMID: 27168765 PMCID: PMC4862166 DOI: 10.1186/s13040-016-0094-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 04/18/2016] [Indexed: 12/01/2022] Open
Abstract
Background The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. Results For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Conclusions Developing the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.
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Affiliation(s)
- Ruowang Li
- Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Scott M Dudek
- Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Dokyoon Kim
- Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Molly A Hall
- Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Yuki Bradford
- Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Peggy L Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin USA
| | - Murray H Brilliant
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin USA
| | - James G Linneman
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin USA
| | | | - Le Bao
- Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Marylyn D Ritchie
- Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA ; Biomedical & Translational Informatics, Geisinger Health System, Danville, Pennsylvania USA
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