Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted...

Area of Science:

• Cardiology
• Biomedical Engineering
• Machine Learning

Background:

• Electrocardiogram (ECG) signals are vital for diagnosing cardiac arrhythmia.
• Missing data in ECG datasets, caused by signal faults, hinders machine learning classification.
• Many existing algorithms require complete data matrices, necessitating imputation methods.

Purpose of the Study:

• To compare imputation methods for missing ECG data.
• To introduce a novel machine learning classifier for imbalanced arrhythmia datasets.
• To evaluate the effectiveness of imputation and classification techniques on real-world data.

Main Methods:

• Comparison of Zero, Mean, PCA-based, and RPCA-based imputation methods for ECG data.
• Development and application of a modified kernel Difference-Weighted KNN classifier (MKDF-WKNN).
• Experimental validation using the UCI arrhythmia database.

Main Results:

• The RPCA-based method effectively handles missing values in ECG arrhythmia datasets, regardless of missing data percentage.
• The proposed MKDF-WKNN classifier demonstrates superior performance compared to existing algorithms (KNN, DS-WKNN, DF-WKNN, KDF-WKNN) on imbalanced datasets.
• Improved classification accuracy was observed for datasets with imputed missing values.

Conclusions:

• RPCA-based imputation is a reliable approach for addressing missing data in ECG signals.
• The MKDF-WKNN classifier offers enhanced accuracy for imbalanced cardiac arrhythmia classification.
• The combined approach of effective imputation and advanced classification improves diagnostic capabilities.