ArticleHealth information science and systems2024
MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis.
Article in Health information science and systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Multi-task aquatic toxicity prediction model based on multi-level features fusion.Journal of advanced research · 2025Article
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- Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.Journal of translational medicine · 2024Article
- MSA-MaxNet: Multi-Scale Attention Enhanced Multi-Axis Vision Transformer Network for Medical Image Segmentation.Journal of cellular and molecular medicine · 2024Article
- SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment.BMC genomics · 2024Article
- LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography.Journal of translational medicine · 2024Article
- Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach.Journal of cellular and molecular medicine · 2024Article
- LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity.Journal of cellular and molecular medicine · 2024Article
- Predicting potential microbe-disease associations based on dual branch graph convolutional network.Journal of cellular and molecular medicine · 2024Article
- Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.Scientific reports · 2024Article
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9 authors.
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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 (
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