Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals.
Electrocardiographic (ECG) signals are affected by several kinds of artifacts that may hide vital signs of interest. In this study we apply independent component analysis (ICA) to isolate motion artifacts. Standard or instantaneous ICA, which is currently the most addressed ICA model within the context of artifact removal, is compared to two other ICA techniques. The first technique is a frequency domain approach to convolutive mixture separation. The second is based on temporally constrained ICA, which enables the estimation of only one component close to a particular reference signal. Performance indexes evaluate ECG complex enhancement and relevant heart rate errors. Our results show that both convolutive and constrained ICA implementations perform better than standard ICA, thus opening up a new field of application for these two methods. Moreover, statistical analysis reveals that constrained ICA and convolutive ICA do not significantly differ concerning heart rate estimation, even though the latter overcomes the former in ECG morphology recovery.
Department of Electrical Systems and Automation, University of Pisa, Pisa, Italy. matteo.milanesi@ing.unipi.it
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Milanesi
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Martini
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Vanello
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Positano
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Santarelli
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Landini
D016477:Artifacts; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D009038:Motion; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted