ArticleBioMed research international2020
Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Time-series ECG Imputation Using a Pattern-Based Masking Framework.medRxiv : the preprint server for health sciences · 2026Article
- Novel biomarkers of crescentic glomerulonephritis identified by urinary proteomics.Clinical kidney journal · 2025Article
- Article
- Risk factors and outcomes of bronchopleural fistula after bronchoplasty in patients with non-small cell lung cancer: a retrospective multivariate analysis.Translational lung cancer research · 2022Article
- Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model.Computational and mathematical methods in medicine · 2021Article
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7 authors.
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Abstract
Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using some machine learning methods. In practice, the ECG datasets are usually with multiple missing values due to faults or distortion. Unfortunately, many established algorithms for classification require a fully complete matrix as input. Thus it is necessary to impute the missing data to increase the effectiveness of classification for datasets with a few missing values. In this paper, we compare the main methods for estimating the missing values in electrocardiogram data, e.g., the "Zero method", "Mean method", "PCA-based method", and "RPCA-based method" and then propose a novel KNN-based classification algorithm, i.e., a modified kernel Difference-Weighted KNN classifier (MKDF-WKNN), which is fit for the classification of imbalance datasets. The experimental results on the UCI database indicate that the "RPCA-based method" can successfully handle missing values in arrhythmia dataset no matter how many values in it are missing and our proposed classification algorithm, MKDF-WKNN, is superior to other state-of-the-art algorithms like KNN, DS-WKNN, DF-WKNN, and KDF-WKNN for uneven datasets which impacts the accuracy of classification.
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