An automatic method for separation and identification of Biomedical Signals from Convolutive Mixtures by Independent Component Analysis in the Frequency Domain

In this study we propose an automatic method for solving convolutive mixtures separation. The independent components are extracted by frequency domain analysis, where the convolutive model can be solved by instantaneous mixing model approach. The signals are reconstructed back in the observation space resolving the ICA model ambiguities. Simulations are carried out to test the validity of the proposed method in convolutive mixtures of electrocardiographic (ECG) and electromyographic (EMG) signals. The algorithm is also tested on real ECG and EMG acquisitions derived from wearable systems.