Key result
Genetic based Principal Component Analysis (GPCA) extracted better features for ischemic beat classification compared to linear PCA.
Why the study?
Does Genetic based Principal Component Analysis (GPCA) improve ischemic beat classification compared to linear PCA in ECG signals?
Does Genetic based Principal Component Analysis (GPCA) improve ischemic beat classification compared to linear PCA in ECG signals?
A novel Genetic Algorithm based Principal Component Analysis (GPCA) improves feature extraction for automated ischemic beat classification from ECG signals compared to standard linear PCA.
GPCA may advance automated ECG ischemia detection; leaves open clinical utility pending prospective validation.
The ischemic beats from Electrocardiogram (ECG) signal detection are based on the specific part of the beat called the ST segment. The performance of the detection relies heavily on the relevant and efficient feature extraction of the ST segment. In this paper, the Genetic Algorithm (GA) is combined with PCA to extract more relevant features; the proposed method is named as Genetic based Principal Component Analysis (GPCA). The performance GPCA is compared with the linear PCA, and shown that the proposed GPCA extracts better features for ischemic classification.
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Murugan et al. (2010) studied Ischemic beats. Genetic based Principal Component Analysis (GPCA) vs. Linear PCA was evaluated on Ischemic classification performance. Genetic based Principal Component Analysis (GPCA) extracted better features for ischemic beat classification compared to linear PCA.
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