Extraction of cardiac and respiration signals in electrical impedance tomography based on independent component analysis

Electrical Impedance Tomography (EIT) has successive wide range in impedance imaging, but still it is difficult to extract cardiac-related conductivity changes and respiratory-related conductivity changes in spontaneous breathing subjects. Quite a few methods are attempted to extract these two signals such as electrocardiogram gated averaging, frequency domain filtering and principal component analysis. However, such methods are not able to take apart these components properly or put some effort in real time imaging and have their own limitations. The purpose of this paper is to introduce a new method in the EIT clinical application field, Independent Component Analysis (ICA) to extract cardiac and respiratory related signals in electrical impedance tomography. Independent component analysis has been introduced to use in electrical impedance tomography but this is the first attempt ever to implement this method to separate these two signals and image those independent conductivity distribution of respiration and cardiac changes independently. Data has been collected from a spontaneous breathing subject. Filtration technique has been used to remove random noise and multi level spatial ICA has been applied to obtain independent component signals which has been later used in reconstruction algorithm for imaging.