System for heart performance characterization and abnormality detection

1. A system for heart performance characterization and abnormality detection, comprising:

an interface conditioned for receiving an electrical signal indicating heart electrical activity of a patient over a heart beat cycle, said electrical signal being acquired at a particular anatomical location;

a gating signal generator conditioned for generating a gating signal to identifying a particular portion within said heart beat cycle, wherein the heart beat cycle comprises more than one individual phase, and the particular portion is one of the phases of the heart beat cycle;

an acquisition device, responsive to said gating signal, conditioned for deriving from the received electrical signal, first and second voltage potentials,

the first voltage potential comprising a voltage potential derived over a time period substantially comprising a heart beat cycle and

the second voltage potential comprising a voltage potential derived over a time period comprising the particular portion within said heart beat cycle, said particular portion being selected for a type of health index calculation; and

a computation processor conditioned for deriving a dynamic impedance representative value by adjusting a baseline impedance value by a ratio of the first and second voltage potentials, said dynamic impedance configured to represent an average impedance at said particular anatomical location over a time period comprising said particular portion within said heart beat cycle.

2. The system according to claim 1 , wherein

said computation processor adaptively selects said calculation type in response to a type of clinical application or diagnosis being performed and

said baseline impedance value represents an averaged impedance of cardiac tissue at said particular anatomical location over a time period comprising a heart beat cycle.

3. The system according to claim 2 , wherein

said computation processor derives said baseline impedance value by averaging a received measured tissue impedance value over said heart beat cycle.

4. The system according to claim 2 , wherein

said computation processor derives said baseline impedance value in response to received measured voltage potential and current values over said heart beat cycle.

5. The system according to claim 1 , wherein

the first and second voltage potentials comprise first and second electrical currents and

said baseline impedance value represents an averaged impedance of cardiac tissue at said particular anatomical location over a time period comprising a heart beat cycle.

6. The system according to claim 1 , wherein

said acquisition device derives the first value representing said first voltage potential by averaging a voltage potential over a time period comprising one or more heart beat cycles and

said acquisition device derives the second value representing said second voltage potential by averaging a voltage potential over a time period comprising the particular portion of one or more heart beat cycles.

7. The system according to claim 1 , wherein

said computation processor derives said dynamic impedance representative value using a function of the form

R

i

⁡

(

t

)

=

R

0

⁡

(

t

)

·

U

i

⁡

(

t

)

U

0

⁡

(

t

)

where, U o (t) and U i (t) represent average voltage potentials across impedance R o (t) and R i (t) respectively and R o (t) is said baseline impedance value and R i (t) is said dynamic impedance.

8. The system according to claim 1 , wherein

said computation processor generates a health status indicator by multiplying said dynamic impedance by a predetermined correction factor to determine a normalized value for said dynamic impedance, said predetermined correction factor being derived from empirical measurement.

9. The system according to claim 8 , wherein

said predetermined correction factor being derived from at least one of, (a) patient specific empirical measurement and (b) empirical measurements from patients having patient substantially compatible age, gender, height and weight characteristics.

10. The system according to claim 8 , wherein

said computation processor generates a health status indicator representing dynamic variation in dynamic impedance by subtracting said baseline impedance from said dynamic impedance.

11. The system according to claim 1 , wherein

said computation processor monitors a patient by substantially continuously deriving said dynamic impedance representative value.

12. The system according to claim 1 , wherein

said computation processor compares said dynamic impedance representative value obtained on a first occasion with a corresponding dynamic impedance representative value of the patient determined at said particular anatomical location on a second occasion.

13. The system according to claim 12 , wherein

said first and second occasions are at least one of, (a) pre- and post treatment and (b) different hospital visits.

14. The system according to claim 1 , including

a repository of information associating a plurality of ranges of dynamic impedance values at one or more particular anatomical locations with corresponding particular medical conditions wherein

said computation processor uses said information in comparing said dynamic impedance representative value with said ranges and identifying a particular medical condition.

15. The system according to claim 1 , including

a self learning network,

employing training datasets for deriving a correction factor for determining a normalized absolute value for said dynamic impedance and

generating a health status indicator by multiplying said dynamic impedance by the derived correction factor to determine a normalized value for said dynamic impedance.

16. The system according to claim 1 , wherein the computation processor is conditioned to select the type of health index calculation and derive a health index value for the particular portion using the dynamic impedance representative value.

17. The system according to claim 16 , wherein the computation processor is further-conditioned to identify a particular medical condition by mapping dynamic impedance values and health index values to corresponding value ranges associated with medical conditions.