Method and system for signal separation during multivariate physiological monitoring

Abstract

Multiple electrode contacts make electrical connections to the anterior and/or posterior chest for multivariate characterization of the electrical activation of the heart. A central processing unit derives synthetic composite electrographic signals as well as flag signals for specific purposes. A preferred embodiment uses this system to trigger or gate magnetic resonance imaging, eliminating or reducing problems from small or inverted R-waves, lead detachment, noise, flow signal, gradient changes, and rhythm changes, more reliably flagging the onset of electrical activation of the ventricles. Additional derived data are ST-segment shifts, filling times, and respiratory cycle. Filling times may be used for greatly improved imaging in the presence of rhythm disturbances, such as atrial fibrillation. Respiratory cycle may be used as a respiratory trigger to control for the effects of breathing on the heart position and image quality.

1 . A method for determining a condition associated with at least part of a biological organ by separating desired signals produced by the at least part of the biological organ from superimposed signals acquired during monitoring via a plurality of signal sensors at different positions in relation to the at least of part of the biological organ, comprising the steps of:
 acquiring multivariate signals relative to the at least of part of the biological organ via the plurality of signal sensors, wherein the signals acquired on each of the signal sensors reflects the respective sensitivity of the particular signal sensor to multiple signal sources, including one or more desired signals and one or more superimposed contaminant signals;   inputting the acquired signals into a data processor that performs the steps of:
 converting the acquired signals into signal data upon which mathematical operations are performed by representing the acquired physiologic signals as an observation data set O(t) whose values are representative of the signals acquired by the corresponding sensors; 
 accessing a separability operator S that includes separation coefficients; 
 applying the separability operator S to the observation data set O(t) to produce an output; 
 extracting one or more output signals from the output to determine a condition associated with the at least part of the biological organ; and 
 enabling the physiological monitoring of the condition associated with the at least part of the biological organ; 
   wherein the separation coefficients collectively specify a model of the conditions of physiologic and external signal sources encountered during data acquisition; and   wherein one or more members of the observation data set O(t) are partially correlated.   
   
   
       2 . The method as in  claim 1  wherein the output represents coverage of electrographic signals beyond a 12-lead. 
   
   
       3 . The method of  claim 1 , further comprising training the separability operator S for a particular physiologic monitoring arrangement, through the steps of:
 selecting a template signal T(t) associated with a training sensor as a measure of success;   acquiring a new observation data set O(t) from the training sensor;   initializing the separability operator S coefficients;   redefining the coefficients of the separability operator S by obtaining a result signal R(t) based on the separability operator S and the observation data set O(t); and   adjusting the coefficients of the separability operator S based on a minimization of an error function E 1  calculated as the difference between a channel of the result signal R(t) and the template signal T(t).   
   
   
       4 . The method of  claim 3 , further comprising the step of:
 correcting for DC offsets in each sensor through the steps of:
 expanding the separability operator S to support one or more additional degree(s) of freedom; and 
 appending one or more pre-selected value(s) to the observation data set O(t). 
   
   
   
       5 . The method of  claim 1 , wherein the desired signals represent contributions to an overall signal from regions related to the at least of part of the biological organ, and signals from other regions are treated as contaminants with respect to each of the desired signals. 
   
   
       6 . The method of  claim 1 , wherein said step of acquiring comprises the step of:
 extracting from the acquired multivariate signals pre-selected types of signals through comparison of the pre-selected types of signals acquired on two or more signal channels to isolate a localized signal.   
   
   
       7 . The method of  claim 1 , further comprising the step of:
 externally generating one or more signals containing information relative to pre-selected types of external signal sources.   
   
   
       8 . The method of  claim 1  further comprising the step of:
 coupling each of the multivariate signals to one of the plurality of signal sensors by a plurality of electrically conducting leads of similar trajectory, not all of which are in electrical contact with the one of the plurality of physiologic signal sensors;   acquiring a common signal in each lead attributable to one of the multiple signal sources that is external to the at least part of the biological organ; and   extracting the common signal from the acquired multivariate physiologic signals.   
   
   
       9 . The method of  claim 1 , further comprising the step of:
 generating from the one or more output signals selected from the group consisting of a synthetic ECG signal including indications of cardiac electrical activity, respiratory activity, and motion.   
   
   
       10 . A system for determining a condition associated with at least part of the biological organ by separating desired signals relating to the at least part of the biological organ from superimposed contaminant signals acquired during monitoring, comprising:
 a plurality of sensors adapted to be located at different positions relative to the at least part of the biological organ for acquiring multivariate signals, wherein the signals acquired on each of the sensors reflects the respective sensitivity of the particular signal sensor to multiple signal sources, including one or more desired signal(s) and one or more superimposed signal(s);   a separability operator that includes separation coefficients;   a data processor in data communication with the plurality of sensors and executing the steps of:
 converting the acquired signals into signal data upon which mathematical operations are performed by representing the acquired signal data as an observation data set O(t) whose values are representative of the physiologic signals acquired by the corresponding sensors; 
 applying the separability operator S to the observation data set O(t) to produce an output; 
 extracting one or more output signals from the output to determine a condition associated with the at least part of the biological organ; and 
 enabling monitoring of the condition associated with the at least part of the biological organ; 
   wherein the separation coefficients collectively specify a model of the conditions of one or more signal sources encountered during data acquisition; and   wherein one or more members of the observation data set O(t) are partially correlated.   
   
   
       11 . The system as in  claim 10  wherein the output represents coverage of electrographic signals beyond a 12-lead. 
   
   
       12 . The system of  claim 10 , wherein the data processor further trains the separability operator S for a particular monitoring arrangement by performing the steps of:
 selecting a template signal T(t) associated with a training sensor as a measure of success;   acquiring a new observation data set O(t) from the training sensor;   initializing the separability operator S;   redefining the coefficients of the separability operator S by obtaining a result signal R(t) based on the separability operator S and the observation data set O(t); and   adjusting the coefficients of the separability operator S based on a minimization of an error function E 1  calculated as the difference between a channel of the result signal R(t) and the template signal T(t), the error function being computed as   
     
       
         
           
             
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       13 . The system of  claim 12 , wherein the data processor further executes the steps of:
 selecting multiple template signals T(t); and   computing the error function E as a sum of independent error terms,   
     
       
         
           
             
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       where E i  is a component error function for component i. 
     
   
   
       14 . The system of  claim 12 , wherein the data processor further executes the step of:
 correcting for DC offsets in each sensor through the steps of:
 expanding the separability operator S to support one or more additional degree(s) of freedom; and 
 appending one or more pre-selected value(s) to the observation data set O(t). 
   
   
   
       15 . The method as in  claim 4  wherein the output measures localized deviations from the template signal. 
   
   
       16 . The method as in  claim 1  further comprising the steps of:
 applying pre-selected selection criteria to choose a subset of the acquired signals and another subset of the output to track over time.   
   
   
       17 . The system as in  claim 10  wherein the data processor further executes the step of:
 applying pre-selected selection criteria to choose a subset of the acquired signals and another subset of the output to track over time.