Cardiography system and method using automated recognition of hemodynamic parameters and waveform attributes

Abstract

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.

1 . A cardiography system for automated recognition of hemodynamic parameters and waveform attributes comprising:
 at least one sensor adapted to provide at least one waveform signal and a hemodynamic parameter input;   a knowledge base configured to provide data corresponding to various disease states;   a processing device in operable communication with said at least one sensor and said knowledge base, said processing device configured to receive said at least one waveform signal and said hemodynamic parameter input, identify waveform attributes on said waveform signal, measure said waveform attributes, and measure said hemodynamic parameter input; cross-reference said waveform attributes and said hemodynamic parameter input with said knowledge base, and output a suggested likelihood of a particular disease state based on said cross-referencing.   
   
   
       2 . The cardiography system of  claim 1  further comprising a display device configured to display at least said output. 
   
   
       3 . The cardiography system of  claim 1  wherein said knowledge base further comprises goal directed therapies correlated with particular disease states. 
   
   
       4 . The cardiography system of  claim 3  further comprising said processing device configured to output at least one suggested goal directed therapy based on the suggested likelihood of a particular disease state. 
   
   
       5 . The cardiography system of claim I wherein said waveform signal is selected from the group consisting of an ICG signal, an ECG signal and a PCG signal. 
   
   
       6 . The cardiography system of  claim 1  wherein said hemodynamic parameter input is selected from the group consisting of thoracic fluid content, heart rate, pre-ejection period, left ventricular ejection time, systolic time ratio, isovolumic relaxation time, stroke volume, stroke volume index, cardiac output, cardiac index, blood pressure, Heather Index, rate pressure product, ejection fraction, end diastolic volume, pulmonary artery occlusion pressure, central venous pressure and systemic vascular resistance. 
   
   
       7 . The cardiography system of  claim 1  wherein said knowledge base comprises waveforms, waveform attributes and hemodynamic parameters. 
   
   
       8 . The cardiography system of  claim 1  wherein said knowledge base comprises waveforms, waveform attributes and hemodynamic parameters that have been associated with systolic heart failure. 
   
   
       9 . The cardiography system of  claim 1  wherein said knowledge base comprises waveforms, waveform attributes and hemodynamic parameters that have been associated with diastolic heart failure. 
   
   
       10 . The cardiography system of  claim 1  wherein said processing device is selected from the group consisting of embedded microprocessors, digital signal processors, personal computers, laptop computers, notebook computers, palm top computers, network computers, Internet appliances, and processor-controlled devices configured to store data and software. 
   
   
       11 . The cardiography system of  claim 2  wherein said display device is selected from the group consisting of computer monitor, flat-screen display, projector, printing device, and audible device. 
   
   
       12 . The cardiography system of  claim 2  wherein said display device further comprises user input devices configured to communicate with said display device and said processing device. 
   
   
       13 . The cardiography system of  claim 1  further comprising said processing device is configured to create an ensemble average waveform based on said waveform signal. 
   
   
       14 . The cardiography system of  claim 13  further comprising said processing device is configured to identify and measure waveform attributes on said ensemble average waveform. 
   
   
       15 . The cardiography system of  claim 14  further comprising said processing device is configured to cross-reference said waveform attributes from said ensemble average waveform with data in said knowledge base. 
   
   
       16 . The cardiography system of  claim 15  further comprising said processing device is configured to output a suggested likelihood of a particular disease state. 
   
   
       17 . The cardiography system of  claim 1  wherein said at least one sensor comprises a first sensor adapted to generate said waveform signal and a second system adapted to provide said hemodynamic parameter input. 
   
   
       18 . A method for automated recognition of hemodynamic parameters and waveform attributes to assess disease states comprising:
 providing at least one sensor for generating at least one waveform signal and a hemodynamic parameter input;   providing a knowledge base having data corresponding to various disease states;   providing a processing device operably communicating with said at least one sensor and said knowledge base, said processing device for receiving said at least one waveform signal and said hemodynamic parameter input, identifying waveform attributes on said waveform signal, measuring said waveform attributes, measuring said hemodynamic parameter input, accessing said knowledge base, cross-referencing said waveform attributes with data in said knowledge base, cross-referencing said hemodynamic parameter input with data in said knowledge base, and outputting a suggested likelihood of a particular disease state based on said cross-referencing.   
   
   
       19 . The method of  claim 18  further comprising providing a display device for displaying at least said output. 
   
   
       20 . The method of  claim 19  further comprising said knowledge base further providing goal directed therapies correlated with particular disease states. 
   
   
       21 . The method of  claim 20  further comprising said processing device outputting at least one suggested goal directed therapy based on the suggested likelihood of a particular disease state. 
   
   
       22 . The method of  claim 18  wherein said waveform signal is selected from the group consisting of an ICG signal, an ECG signal and a PCG signal. 
   
   
       23 . The method of  claim 18  wherein said hemodynamic parameter input is selected from the group consisting of thoracic fluid content, heart rate, pre-ejection period, left ventricular ejection time, systolic time ratio, isovolumic relaxation time, stroke volume, stroke volume index, cardiac output, cardiac index, blood pressure, Heather Index, rate pressure product, ejection fraction, end diastolic volume, pulmonary artery occlusion pressure, central venous pressure and systemic vascular resistance. 
   
   
       24 . The method of  claim 18  wherein said knowledge base comprises waveforms, waveform attributes and hemodynamic parameters. 
   
   
       25 . The method of  claim 18  wherein said knowledge base comprises waveforms, waveform attributes and hemodynamic parameters that have been associated with systolic heart failure. 
   
   
       26 . The method of  claim 18  wherein said knowledge base comprises waveforms, waveform attributes and hemodynamic parameters that have been associated with diastolic heart failure. 
   
   
       27 . The method of  claim 18  wherein said processing device is selected from the group consisting of embedded microprocessors, digital signal processors, personal computers, laptop computers, notebook computers, palm top computers, network computers, Internet appliances, and processor-controlled devices configured to store data and software. 
   
   
       28 . The method of  claim 19  wherein said display device is selected from the group consisting of computer monitor, flat-screen display, projector, printing device, and audible device. 
   
   
       29 . The method of  claim 19  wherein said display device further comprises user input devices configured to communicate with said display device and said processing device. 
   
   
       30 . The method of  claim 18  further comprising said processing device creating an ensemble average waveform based on said waveform signal. 
   
   
       31 . The method of  claim 30  further comprising said processing device identifying and measuring waveform attributes on said ensemble average waveform. 
   
   
       32 . The method of  claim 31  further comprising said processing device cross-referencing said waveform attributes from said ensemble average waveform with data in said knowledge base. 
   
   
       33 . The method of  claim 32  further comprising said processing device outputting a suggested likelihood of a particular disease state. 
   
   
       34 . The method of  claim 18  wherein said at least one sensor comprises a first sensor for generating said waveform signal and a second sensor for providing said hemodynamic parameter input.