Key result
An automated classification method using Discrete Wavelet Transform, Discrete Cosine Transform, and Principal Component Analysis with a k-Nearest Neighbor classifier was proposed to detect CAD from ECG.
Why the study?
Does an automated classification system using linear techniques (k-NN) accurately detect CAD from ECG signals compared to an SVM classifier?
Population
Normal and Coronary Artery Disease (CAD) ECG signals
Comparison
Automated classification using linear techniques vs Support Vector Machine (SVM) classifier
Authors
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Proposed ECG-based CAD classifier requires validation; leaves open clinical accuracy versus SVM.
Does an automated classification system using linear techniques (k-NN) accurately detect CAD from ECG signals compared to an SVM classifier?
Automated classification using linear techniques like k-NN and SVM may assist in the early and precise recognition of CAD from ECG signals.
Swasthik et al. (2017) studied Coronary Artery Disease (CAD). Automated classification using linear techniques (DWT, DCT, PCA, k-NN) vs. Support Vector Machine (SVM) classifier was evaluated on Classification of normal and CAD ECG signals. An automated classification method using Discrete Wavelet Transform, Discrete Cosine Transform, and Principal Component Analysis with a k-Nearest Neighbor classifier was proposed to detect CAD from ECG.
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