referring to fig1 and 2 , there is shown a cvg waveform 12 and a pcg waveform 14 in accordance with the system and method of the present invention . both figures depict heart valve activity in cvg waveform 12 . the pep is determined by identifying the time period between the starting point of the qrs complex based on an ecg signal , labeled as the q point , and the starting point of the mechanical systole as marked by the initial deflection of the systolic waveform based on the ecg signal coincident with the opening of the aortic valve or the onset of left ventricular ejection into the aorta , labeled as the b point . the lvet is determined by identifying the time period between the end of the pep and the closure of the aortic valve when ejection ends , labeled as the x point . both figures also depict heart valve activity in pcg waveform 14 , where known devices and methods are used to monitor and record sounds associated with the aortic valve opening , labeled as s 1 , and closing , labeled as s 2 . while fig1 and 2 depict pcg waveforms , those skilled in the art can appreciate that waveforms generated from any signals derived from heart valve activity can be depicted in relation to cvg waveforms . referring to fig3 , one embodiment of the system in accordance with the present invention includes a display device 16 used to display cardiovasculograms and a processing device 18 . processing device is used to receive inputs from a sensor 20 hooked to a patient , generate cardiovasculograms and communicate with display device 16 . those skilled in the art can appreciate that display device 16 may include any type of device for presenting visual information such as , for example , a computer monitor or flat - screen display . display device 16 may be equipped with user input devices , such as buttons for silencing audible alarms , erasing visual alarms or a combination thereof . in one embodiment , sensor 20 includes electrodes for measuring icg signals , pcg signals and ecg signals , microphones for measuring and recording heart sounds , blood pressure monitors , signals representing central venous pressure , finger plethysmographs and the like . while fig3 depicts one sensor 20 , in another embodiment more than one sensor 20 is used . here , a first sensor is used to convert physiological data from a patient being monitored to a waveform having particular waveform attributes representing the physiological data . the first sensor can be an icg system , an ecg system , a pcg system , or a combination thereof . an output generated from the first sensor can be a physical output , including but not limited to graphical display , printout , and the like . the output from the first sensor can alternately or simultaneously be an electrical output signal configured to be received by processing device 18 . a second sensor is used to measure other hemodynamic parameters from the patient being monitored and convert them into a second output . these hemodynamic parameters include , but are not limited to thoracic fluid content ( tfc ), heart rate ( hr ), pre - ejection period ( pep ), left ventricular ejection time ( lvet ), isovolumic relaxation time ( ivrt ), stroke volume ( sv ), cardiac output ( co ), blood pressure , heather index ( hi ), and systemic vascular resistance ( svr ). the second output generated from the second sensor can be a physical output , including but not limited to graphical display , printout , and the like . the second output from the second sensor can alternately or simultaneously be an electrical output signal configured to be received by processing device 18 . those skilled in the art can appreciate that the number and use of the sensors can vary . those skilled in the art can appreciate that the system in accordance with the present invention may include stationary systems used in intensive care units or emergency rooms in hospitals , or may comprise portable units for use by emergency medical technicians in ambulances , at the scene of accidents , and when responding to other emergency situations . processing device 18 includes cardiovasculogram criteria for the diagnosis of heart failure based upon changes noted in the normal contours and dimensions of the typical cardiovasculogram waveform . while clinicians often use a subjective pattern recognition methodology for determination of aberrancy , the present invention includes objective criteria that can be utilized for a more exacting analysis . these objective criteria are useful in the development of a computerized algorithmic analysis of cardiovasculogram waveforms . those skilled in the art can appreciate that the system may contain criteria for diagnoses of other disease states . referring now to fig4 , there are shown waveform attributes , including baseline thoracic impedance ( z 0 ), atrial wave ( a ), aortic valve opening ( b ), maximum aortic flow ( c ) ( also represented as dz / dt max ), aortic valve closing ( x ); pulmonic valve closing ( y ), mitral valve opening ( o ); pre - ejection period ( pep ); ventricular ejection time ( vet ), isovolumic relaxation time ( ivrt ), and ventricular filling time ( ft ). these waveform attributes can be used to build the cardiovasculogram criterion for diagnosing heart failure based upon changes noted in the normal contours and dimensions of typical cardiovasculogram waveforms . still referring to fig4 , the c - wave is the major upward deflection in impedance seen during systolic phase of the cardiac cycle that peaks at the point of dz / dt max . it is seen as the first deflection from baseline thoracic impedance ( z 0 ) after the a - wave , beginning with the b point and ending with the x point . during systole , the form of the c - wave is based on the force of ventricular contraction and the resultant aortic pulse pressure wave generated when blood is transferred out of the ventricle and into the aorta . the dz / dt max point of the c - wave is correlated with the peak aortic blood flow . systolic function is generally defined by the shape , depth , and duration of the c - wave . normal amplitudes for the c - wave will vary depending on the system used but may range from 1 . 05 to 2 . 70 . the o - wave is defined by the diastolic portion of the cardiac cycle and peaks at the point of mitral valve opening , shown as the 0 point on fig4 . the filling of the vena cava and pulmonary vein during the early phase of diastole results in the up - slope of the impedance signal . the ventricular filling phase begins when the tricuspid and mitral valves open . during the terminal portion of the o - wave , there is an increase in the impedance signal and a return to baseline thoracic impedance ( z 0 ) at the end of diastole as the venous system empties into the heart . accordingly , this waveform reflects the events of diastole , including cardiac filling and venous return . lvet begins at the end of the pep when the aortic valve opens . the lvet ends at the closure of the aortic valve when ejection ends as determined by the dz / dt waveform . a typical normal value for lvet is about 295 ± 26 msec . ivrt is a measure of diastolic function and active ventricular relaxation . ivrt is represented as the x to o period , which begins with the aortic valve closure and ends at the point of the maximum second deflection . a typical normal value for ivrt is less than 125 msec . referring now to fig5 , there is shown one embodiment for specific criteria used to interpret a heart failure waveform based on the foregoing attributes in accordance with the present invention . in one embodiment , specific criteria for determination of the proportional changes in the c - wave and o - wave in a patient with decompensated heart failure are based upon the correlation of the o / c ratio and the pulmonary capillary wedge pressure ( pcwp ). a typical normal range of the o / c ratio , i . e . 0 . 43 ± 0 . 09 , was correlated with a pcwp of about 10 to 12 mmhg , which is within typical normal pcwp range . increases in the o / c ratio greater than 0 . 6 ± 0 . 12 indicate pathologic congestion . this level of o / c ratio correlates with a pcwp of about 20 to 25 mmhg , which is considered the break point for the onset of pulmonary edema formation . therefore , an o / c ratio of about greater than 0 . 6 can be used to indicate cardiopulmonary congestion as seen in acute decompensated heart failure . referring now to fig6 there is shown another embodiment for specific criteria used to interpret a heart failure waveform in accordance with the present invention . systolic heart failure is typically due to failure of in contraction strength of the myocardium during systole . systolic contractile force can be viewed from a basic physical perspective , such as force = mass × acceleration , where systolic contractile force is defined as the amount of blood ejected , mass , times the velocity at which it is ejected , acceleration . in a normal cvg waveform , cardiac systole manifests as a sharp peaking c - wave ( as shown in fig4 ). the upslope of the c - wave and the length of the base of the lvet wave are both independently correlated with a general myocardial contractile state . a cvg waveform pattern with a broad blunted c - wave is characteristic of general heart failure in a patient and can be used to help differentiate that condition . the typically normal values for the c - wave and lvet as previously discussed herein are used in the determination of aberrancy . a decompensated systolic heart failure condition is expected to physiologically lead to congestion within the venous side of the circulatory system . this congestion can be correlated with increasing thoracic fluid content and increasing baseline thoracic impedance ( z 0 ) in the cvg waveform as depicted in fig6 . large o - waves with elevated peaks are common in cvg waveforms depicting decompensated heart failure . referring now to fig7 , there is depicted another embodiment for specific criteria used to interpret a heart failure waveform in accordance with the present invention . a correlation between left ventricular hypertrophy measured in mv and active ventricular relaxation time measured in seconds can be used when assessing diastolic heart failure . diastolic heart failure is caused by a limitation in ventricular compliance and relaxation , resulting in a limitation in cardiac filling during diastole . the diastolic ivrt can be measured from the o - wave of the cvg . prolongation in the ivrt is indicated by a general pattern of a widening of the base of the o - wave , which suggests a diagnosis of diastolic heart failure . as shown in fig7 , ivrt by cvg waveform analysis can be correlated with the degree of left ventricular hypertrophy , a major determinant of diastolic dysfunction . when there is concurrent venous congestion due to the delay in cardiac filling during diastole combined with the occurrence of decompensation , the o - wave pattern may also have an elevated peak . this combination of factors provides a general cvg waveform pattern characterized by an overall substantial and prolonged o - wave with a large area under the curve . this type of o - wave may be indicative of decompensated diastolic heart failure . normal values for the o - wave and ivrt , as previously discussed , can be used for determination of aberrancy . as illustrated in the flowchart depicted in fig8 , one method for using cvg criterion for the diagnosis of heart failure in accordance with the present invention includes : inputting an icg signal 30 ; inputting an ecg signal 32 ; compiling an r - wave triggered ensemble average based on ecg and icg signals 33 ; producing a cvg waveform ; identifying the c , or systolic , wave 34 ; identifying the o , or diastolic , wave 35 ; measuring c - wave attributes 36 ; measuring other supporting parameters 37 ; measuring o - wave attributes 38 ; inputting knowledge base for heart failure classification 39 ; cross - referencing c - wave and o - wave attributes and other supporting parameters with knowledge base 40 ; suggesting the likelihood of systolic heart failure 42 ; and suggesting the likelihood of diastolic heart failure 44 . those skilled in the art can appreciate that cross - referencing of the measured attributes with the knowledge base can be accomplished by bayesian probability statistics , fuzzy logic , or other advanced mathematical techniques . while fig8 shows the use of an r - wave triggered ensemble average , those skilled in the art can appreciate that other waveform averaging techniques , non - averaged waveforms and / or various compilations of waveforms and / or multi - beat sequences of waveforms can be used in accordance with the present invention . the term knowledge base as used herein is defined as a database , a computer program , any type of data readable by an electronic medium , any data ( whether or not alpha - numeric ) that can be indexed and stored in an electronic medium , data stored in hard copy that can be accessed by or entered into the system of the present invention by users , and the like . in the flowchart depicted in fig8 , the c - wave attributes and o - wave attributes that could be measured in steps 36 and 38 include , but are not limited to , amplitude , duration , upward slope , downward slope , shape , depth , area , tone and presence of additional peaks . other supporting parameters that could be measured in step 37 include , but are not limited to , thoracic fluid content ( tfc ), heart rate ( hr ), pre - ejection period ( pep ), left ventricular ejection time ( lvet ), systolic time ratio , isovolumic relaxation time ( ivrt ), stroke volume ( sv ), stroke volume index , cardiac output ( co ), cardiac index , blood pressure , heather index ( hi ), rate pressure product , ejection fraction , end diastolic volume , pulmonary artery occlusion pressure , central venous pressure and systemic vascular resistance ( svr ). fig8 depicts the use of pcg signals and other supporting parameters to confirm heart valve activity in the cvg waveform for illustrative purposes only . those skilled in the art can appreciate that pcg signals and / or other supporting parameters could be used alone or in combination to confirm heart valve activity in cvg waveforms . in step 40 , waveform attributes and other supporting parameters are cross - referenced against a knowledge base containing known attributes of heart failure classifications . cross - reference logic for identifying likelihood of systolic heart failure and diastolic heart failure , including assessing c - wave parameters , o - wave parameters , and supporting parameters , is included in processing device 18 . in one embodiment , the logic could also be used to assess cross - factors . one exemplary cross - factor is the ratio of the o - wave height to c - wave height . those skilled in the art can appreciate that cross - referencing of the measured attributes with the knowledge base can be accomplished by bayesian probability statistics , fuzzy logic , or other advanced mathematical techniques . in one embodiment , the suggestion of the likelihood of systolic heart failure in step 42 or diastolic heart failure in step 44 could be presented with confidence information in a numeric , graphical , bar presentation , or other format . in another embodiment , the suggestion of the likelihood of systolic heart failure in step 42 or diastolic heart failure in step 44 could be associated with the likelihood or coincidence of waveform attributes being associated with a standardized heart failure classification system such as the new york heart association ( nyha ) classification system . referring now to fig9 , processing device 18 illustrates typical components of a processing device . processing device 18 includes a local memory 46 , a secondary storage device 54 , a processor 56 , a user interface device 60 and an output device 58 . local memory 46 may include random access memory ( ram ) or similar types of memory , and it may store one or more applications 48 , including system software 50 , and a web server 52 , for execution by processor 56 . local memory 46 is generally located in individual pieces of equipment used to monitor cardiac performance of patients . secondary storage device 54 may include a hard disk drive , floppy disk drive , cd - rom drive , or other types of non - volatile data storage . the local cache that includes a patient &# 39 ; s cvg data may be stored on secondary storage device 54 . processor 56 may execute system software 50 and other applications 48 stored in local memory 46 or secondary storage 54 . processor 56 may execute system software 50 in order to provide the functions described in this specification including , but not limited to , measuring , reporting , displaying and comparing cardiovasculograms . user interface device 60 may include any device for entering information into processing device 18 , such as a keyboard , mouse , cursor - control device , touch - screen , infrared , microphone , digital camera , video recorder , or any other instrument or device necessary to measure , report , display and compare cardiovasculograms . output device 58 may include any type of device for presenting a hard copy of information , such as a printer , and other types of output devices including speakers or any device for providing information in audio form . web server 52 is used to provide access to patient data stored in memory 46 and on secondary storage devices 54 and display the data . web server 52 allows users secure remote access to the system through which they can monitor the status of a patient &# 39 ; s cvg data and access patient data . web server 52 can allow access to a user running a web browser . any web browser , co - browser , or other application capable of retrieving content from a network and displaying pages or screens may be used . examples of processing devices 18 for interacting within the impedance cardiography system include embedded microprocessors , digital signal processors , personal computers , laptop computers , notebook computers , palm top computers , network computers , internet appliances , or any processor - controlled device capable of storing data , system software 50 and any other type of application 48 stored in local memory 46 or accessible via secondary storage device 54 . local memory 46 can further include an application for using the knowledge base for heart failure classification in step 39 of fig8 . this application is used to provide automated recognition of hemodynamic parameters and waveform attributes . one method includes saving waveforms and hemodynamic parameters in local memory 46 to be used in the aforementioned methods as depicted in fig5 - 7 and as templates for future comparison and identification of associated disease states . for example , a patient having cvg waveform 62 as depicted in fig6 was diagnosed by an experienced healthcare professional as having systolic heart failure . a patient having cvg waveform 64 as depicted in fig1 was diagnosed by an experienced healthcare professional as having diastolic heart failure . these waveforms 62 and 64 are stored as waveform and hemodynamic data in local memory 46 . the method illustrated in fig1 can incorporate the stored waveforms and hemodynamic data depicted in fig1 and 11 and includes : measuring cvg waveform and hemodynamic parameters of a new patient 66 ; providing access to a knowledge base of waveform attributes and hemodynamic parameter data stored in local memory 68 ; automatically correlating the new patient cvg waveform attributes and hemodynamic parameters to at least one record stored in local memory 70 ; and guiding a goal directed therapy for a possible disease based on this correlation 72 . while fig8 depicts the application for using the knowledge base for heart failure classification included in processing device 18 , those skilled in the art can appreciate that processing device 18 and knowledge base can be two or more separate systems that communicate with one another via known communication techniques , including but not limited to modem connections , wireless connections , optical connections and the like . in another embodiment , certain waveform attributes may be learned from waveforms associated with disease states , where combinations of these attributes are used to form a template for that disease state . in yet another embodiment , analysis and diagnoses for various disease states as determined by experienced healthcare professionals can be correlated with saved waveforms attributes and hemodynamic parameters . for example , as shown in fig1 , an experienced healthcare professional can input specific information 74 about waveform 62 ( also shown without information in fig1 ) correlated with systolic heart failure . in addition , as shown in fig1 , an experienced healthcare professional can input specific information 76 about waveform 64 ( also shown without information in fig1 ) correlated with diastolic heart failure . these waveforms and hemodynamic data along with additional information can be stored in local memory 46 . when a new waveform is generated , it can be compared to the information stored in local memory 46 and healthcare professionals can utilize all of the information , as well as the waveforms and hemodynamic data , to diagnose a possible disease . in this manner , less experienced healthcare professionals get the benefit of experienced healthcare professionals in recognizing and diagnosing a possible disease based on waveform attributes , hemodynamic parameters and / or other information . in addition , recognition and diagnosis of a possible disease can occur quicker based on past diagnoses . the method can optionally further provide healthcare professionals with assistance in achieving a goal directed therapy . while the waveforms depicted in fig1 , 11 , 13 and 14 are cvg waveforms , those skilled in the art can recognize that this method may be used on any type of waveform . those skilled in the art can also recognize that this method may be used with cvgs that correlate icg signals with any signals derived from heart valve activity , hemodynamic events and any other combination thereof . while the invention has been described with reference to the specific embodiments thereof , those skilled in the art will be able to make various modifications to the described embodiments of the invention without departing from the true spirit and scope of the invention . the terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations . those skilled in the art will recognize that these and other variations are possible within the spirit and scope of the invention as defined in the following claims and their equivalents .
a cardiography system and method using automated recognition of hemodynamic parameters and waveform attributes is provided . the cardiography system and method includes at least one sensor , a knowledge base and a processing device . the at least one sensor provides a waveform signal and a hemodynamic parameter input . the knowledge base includes data corresponding to various disease states . the processing device receives the waveform signal and hemodynamic parameter input from the sensor , identifies waveform attributes on the waveform signal , measures the waveform attributes , accesses the knowledge base , cross - references the waveform attributes and the hemodynamic parameters with data in the knowledge base , and outputs a suggested likelihood of a particular disease state . the knowledge base optionally includes goal - directed therapies associated with particular disease states for providing suggested goal - directed therapies based on the cross - referencing of the waveform attributes and the hemodynamic parameters with the knowledge base .