Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System.

Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals is challenging. However, long-duration ECG data are susceptible to various types of noise during acquisition. To tackle the problem, Subspace Search Variational Mode Decomposition (SSVMD) was proposed, which determines the optimal solution by continuously narrowing the parameter subspace and implements data preprocessing by removing baseline drift noise and high-frequency noise modes.