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

1. A system for automated recognition of hemodynamic parameters and waveform attributes comprising:

an impedance cardiography device including at least one sensor configured to be externally placed on a patient and adapted to noninvasively provide at least one impedance waveform signal;

a knowledge base including known 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 impedance waveform signal and calculate a hemodynamic parameter of the patient therefrom, identify waveform attributes on said waveform signal, measure said waveform attributes, measure said hemodynamic parameter, and cross-reference said waveform attributes and said hemodynamic parameter with said knowledge base data, and output a suggested likelihood of a particular disease state based on said cross-reference.

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 2 wherein said display device is selected from the group consisting of computer monitor, flat-screen display, projector, printing device, and audible device.

4. 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.

5. The cardiography system of claim 1 wherein said knowledge base known data further comprises goal directed therapies correlated with particular disease states.

6. The cardiography system of claim 5 wherein said processing device is configured to output at least one suggested goal directed therapy based on the suggested likelihood of a particular disease state.

7. The cardiography system of claim 1 wherein said impedance waveform signal is selected from the group consisting of an ICG signal, an ECG signal and a PCG signal.

8. The cardiography system of claim 1 wherein said hemodynamic parameter 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.

9. The cardiography system of claim 1 wherein said knowledge base known data comprises a plurality of waveforms, waveform attributes and hemodynamic parameters.

10. The cardiography system of claim 9 wherein said plurality of waveforms, waveform attributes and hemodynamic parameters are associated with systolic heart failure.

11. The cardiography system of claim 9 wherein said plurality of waveforms, waveform attributes and hemodynamic parameters are associated with diastolic heart failure.

12. 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.

13. The cardiography system of claim 1 said processing device being configured to create an ensemble average waveform based on said waveform signal.

14. The cardiography system of claim 13 said processing device being configured to identify and measure waveform attributes on said ensemble average waveform.

15. The cardiography system of claim 14 said processing device further configured to cross-reference said waveform attributes from said ensemble average waveform with said knowledge base known data.

16. The cardiography system of claim 15 said processing device being configured to output a suggested likelihood of a particular disease state.

17. A method for automated recognition of hemodynamic parameters and waveform attributes to assess disease states comprising:

providing an impedance cardiography system for the automated recognition of hemodynamic parameters and waveform attributes to assess disease states;

externally placing a sensor on a patient, said sensor for non-invasively generating an impedance waveform signal;

providing a knowledge base including known 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 impedance waveform signal and calculating a hemodynamic parameter of the patient therefrom, identifying waveform attributes on said waveform signal, measuring said waveform attributes, measuring said hemodynamic parameter, accessing said knowledge base, cross-referencing said waveform attributes with data in said knowledge base, cross-referencing said hemodynamic parameter with data in said knowledge base, and outputting a suggested likelihood of a particular disease state based on said cross-referencing.

18. The method of claim 17 further comprising providing a display device for displaying said outputting.

19. The method of claim 18 further comprising said knowledge base further providing goal directed therapies correlated with particular disease states.

20. The method of claim 19 further comprising said processing device outputting at least one suggested goal directed therapy based on the suggested likelihood of a particular disease state.

21. The method of claim 18 wherein said display device is selected from the group consisting of computer monitor, flat-screen display, projector, printing device, and audible device.

22. The method of claim 18 wherein said display device further comprises user input devices configured to communicate with said display device and said processing device.

23. The method of claim 17 wherein said impedance waveform signal is selected from the group consisting of an ICG signal, an ECG signal and a PCG signal.

24. The method of claim 17 wherein said hemodynamic parameter 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.

25. The method of claim 17 wherein said knowledge base known data comprises waveforms, waveform attributes and hemodynamic parameters.

26. The method of claim 25 wherein said waveforms, waveform attributes and hemodynamic parameters are associated with systolic heart failure.

27. The method of claim 25 wherein said waveforms, waveform attributes and hemodynamic parameters are associated with diastolic heart failure.

28. The method of claim 17 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.

29. The method of claim 17 further comprising said processing device creating an ensemble average waveform based on said waveform signal.

30. The method of claim 29 further comprising said processing device identifying and measuring waveform attributes on said ensemble average waveform.

31. The method of claim 30 further comprising said processing device cross-referencing said waveform attributes from said ensemble average waveform with data in said knowledge base.

32. The method of claim 31 further comprising said processing device outputting a suggested likelihood of a particular disease state.