Feature Selection Algorithm for ECG Signal Analysis

The MATLAB-developed feature selection algorithm is designed to extract optimal feature combinations from ECG (electrocardiogram) signals, effectively reducing feature dimensionality. This algorithm employs techniques such as sequential feature selection, mutual information criteria, or wrapper methods to identify the most discriminative characteristics while eliminating redundant features. The implementation typically involves functions like sequentialfs for forward/backward selection, rankfeatures for scoring features based on statistical measures, and custom evaluation metrics for assessing feature subset performance. This approach enables researchers to process ECG signals more efficiently and analyze data with greater accuracy. By utilizing this feature selection algorithm, we can extract more meaningful information and better understand ECG signal characteristics, contributing to advanced research and development in ECG signal processing technology. This advancement provides enhanced support for medical diagnosis and treatment applications through optimized computational efficiency and improved pattern recognition capabilities.