An automatic method for separation and identification of biomedical signals from convolutive mixtures by independent component analysis in the frequency domain

Authors:
Matteo Milanesi;Nicola Vanello;Vincenzo Positano;Maria Filomena Santarelli;Danilo De Rossi;Luigi Landini
Affiliations:
Department of Electrical Systems and Automation, Faculty of Engineering, University of Pisa, Italy;Department of Electrical Systems and Automation, Faculty of Engineering, University of Pisa, Italy;CNR, Institute of Clinical Physiology, Pisa, Italy;CNR, Institute of Clinical Physiology, Pisa, Italy;Interdepartmental Research Center "E. Piaggio", Faculty of Engineering, University of Pisa, Italy;Interdepartmental Research Center "E. Piaggio", Faculty of Engineering, University of Pisa, Italy
Venue:
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Year:
2005

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Abstract

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.