1. A method of measuring performance and capacity of a networked server, the method comprising:
initializing each client machine of a cluster of client machines with a fixed number of client applications,
wherein each client application uses a different type of operations to communicate with a server;
generating a first feedback process of a current state of said each client machine and overall behavior of the client applications;
enabling said each client application to self adjust its own operation based on the first feedback process; and
setting up a second feedback process for the server in which the server and the cluster of client machines reach a balance point of a client count for the server.
2. The method of claim 1, wherein enabling said each client application to self adjust its own operation comprises
said each client application deciding to change to a different operation type.
3. The method of claim 1, wherein enabling said each client application to self adjust its own operation comprises
said each client application deciding to exit a test to free up resources for other clients, or to increase a number of the client applications running on said each client machine.
4. The method of claim 1, wherein enabling said each client application to self adjust its own operation comprises
said each client application deciding to increase a number of the client applications running on said each client machine.
5. The method of claim 1, wherein enabling said each client application to self adjust its own operation results in said each client machine reaching a balance point of a client application count for said each client machine.
6. The method of claim 5, further comprising
outputting the balance point of a client count for the server and the balance point of a client application count for said each client machine as a result of measuring performance and capacity of the networked server.
7. The method of claim 1, wherein the balance point of a client count for the server is reached when a maximum count of client machines with desired behavior is achieved for the server using the second feedback process.
8. A system to measure the capacity and performance of a networked server, comprising:
at least one cluster of client machines arranged in a client feedback loop, each client machine initialized with a fixed number of client applications,
wherein the client feedback loop generates a current is state of said each client machine and overall behavior of the client applications; and
at least one server coupled to the at least one cluster of client machines in a server feedback loop to reach a balance point of a client count for the at least one server,
wherein each client application uses a different type of operations to communicate with the at least one server, is enabled to self adjust its own operation based on the client feedback loop.
9. The system of claim 8, wherein said each client application includes a self adjustment operation to change to a different operation type.
10. The system of claim 8, wherein said each client application includes a self adjustment operation to exit a test to free up resources for other clients.
11. The system of claim 8, wherein said each client application includes a self adjustment operation to increase a number of the client applications running on said each client machine.
12. The system of claim 8, wherein said each client application includes a self adjustment operation to reach a balance point of a client application count for said each client machine.
13. A non-transitory computer-readable storage medium storing a computer program, the computer program comprising executable instructions that cause a computer to measure performance and capacity of a networked server, the computer program comprising executable instructions that cause a computer to:
initialize each client machine of a cluster of client machines with a fixed number of client applications,
wherein each client application uses a different type of operations to communicate with a server;
generate a first feedback process of a current state of said each client machine and overall behavior of the client applications;
enable said each client application to self adjust its own operation based on the first feedback process; and
set up a second feedback process for the server in which the server and the cluster of client machines reach a balance point of a client count for the server.
14. The storage medium of claim 13, wherein executable instructions that cause a computer to enable said each client application to self adjust its own operation comprise executable instructions that cause a computer to
decide by said each client application to change to a different operation type.
15. The storage medium of claim 13, wherein executable instructions that cause a computer to enable said each client application to self adjust its own operation comprise executable instructions that cause a computer to
decide by said each client application to exit a test to free up resources for other clients.
16. The storage medium of claim 13, wherein executable instructions that cause a computer to enable said each client application to self adjust its own operation comprise executable instructions that cause a computer to
decide by said each client application to increase a number of the client applications running on said each client machine.
17. The storage medium of claim 13, wherein executable instructions that cause a computer to enable said each client application to self adjust its own operation comprise executable instructions that cause a computer to
reach a balance point of a client application count for said each client machine.
18. The storage medium of claim 17, further comprising executable instructions that cause a computer to
output the balance point of a client count for the server and the balance point of a client application count for said each client machine as a result of measuring performance and capacity of the networked server.
19. The storage medium of claim 13, wherein the balance point of a client count for the server is reached when a maximum count of client machines with desired behavior is achieved for the server using the second feedback process.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of pattern recognition for diagnosing the presence of Pulmonary Hypertension and classifying the functional status of patients with that chronic condition using a calculated multiparametric index (MPIPH).
2. A method as in claim 1 wherein said MPI is calculated using end tidal CO2 (ETCO2) cardiopulmonary exercise test related measurements.
3. A method as in claim 1 where in the MPI is computed using the equation
MPIPH=(A\u221235 mmHg)+G*W1+H*W2+I*W3+J*W4+K*W5+L*W6
Where A-L are individual Ranking Parameters and W1-W6 are weighting factors for the particular Ranking Parameters, the weighting factors being determined by either retrospective statistical analysis or by statistical analysis of breath-by-breath cardiopulmonary exercise test data acquired from patients with diagnosed presence of PH prospectively over time.
4. A method as in claim 3 wherein the values for Ranking Parameters A-L are calculated, at least in part, from ETCO2 cardiopulmonary exercise test related measurements.
5. A method as in claim 2 wherein the cardiopulmonary exercise test measurements are gathered from either sub-maximal exercise or peak exercise bouts.
6. A method as in claim 4 wherein the cardiopulmonary exercise test measurements are gathered from either sub-maximal exercise or peak exercise bouts.
7. A method as in claim 2 wherein cardiopulmonary exercise test measurements are displayed during low intensity or peak exercise and stored as data sets, each set being associated with a rest phase, an exercise phase, and a recovery phase.
8. A method as in claim 4 wherein cardiopulmonary exercise test measurements are displayed during low intensity or peak exercise and stored as data sets, each set being associated with a rest phase, an exercise phase, and a recovery phase.
9. A method as in claim 3 wherein one or more Ranking Parameters A-L are determined, in part, by a feature extraction mechanism that computes, as the measured value, the difference between the average value of select variables or ratios of select variables obtained as data at rest, during exercise and during recovery.
10. A method as in claim 4 wherein:
(a) A=the last 30 second average ETCO2 value during rest;
(b) G=the initial, transient rise in ETCO2 after the start of exercise;
(c) H=the delay time between A and the first ETCO2 value during exercise less than A;
(d) I is determined, in part, by a feature extraction mechanism that computes, as the measured value, the slope of the line of regression obtained from select data pairs obtained during sub-maximal exercise;
(e) J represents the maximum drop in ETCO2 during submaximal exercise;
(f) K represents intra-exercise rebound during submaximal exercise;
(g) L represents recovery rebound (one minute); and
(h) the value for one or more weighting factors W1-W6 is calculated, in part, from statistical values in the scientific literature or from breath-by-breath cardiopulmonary exercise test data acquired from patients with diagnosed presence of PH prospectively over time.
11. A method as in claim 3 wherein the statistical values include the normal value (NV) and cutoff point (COP).
12. A method as in claim 2 wherein the measured MPIPH is located and displayed in a time sequential manner on a numeric axis that ranges from positive to negative values.
13. A method as in claim 2 wherein the measured MPIPH is juxtaposed on the NYHAWHO Classification of Functional Status of Patients with Pulmonary Hypertension.
14. A method as in claim 2 wherein the measured MPIPH is used to determine whether the patient exhibits either pulmonary arterial hypertension or pulmonary venous hypertension.
15. A method as in claim 2 further comprising repeating the calculation of MPIPH at spaced intervals for therapy tracking.
16. A method as in claim 2 further comprising using the calculated MPIPH to diagnose the severity of a pulmonary hypertension condition.
17. A method of diagnosing the presence of pulmonary hypertension (PH) in patients using end tidal CO2 (ETCO2) cardiopulmonary exercise test related pressure measurements (PetCO2) comprising:
(a) obtaining a last average value of PetCO2 (mmHg) for a patient before exercise;
(b) obtaining a last average value of PetCO2 (mmHg) during exercise;
(c) determining the difference (b)\u2212(a); and
(d) wherein if (b)\u2212(a)\u22671.8 mmHg PH is determined not to be present.
18. A method as in claim 17 wherein said average values are based on a time span of about 30 seconds.
19. A method as in claim 17 wherein said exercise is submaximal.
20. A method of diagnosing the presence of pulmonary hypertension (PH) in patients using end tidal CO2 (ETCO2) cardiopulmonary test related pressure (PetCO2) measurements comprising:
(a) creating a measure PetCO2 waveform of values of end tidal CO2 pressure (PetCO2) in mmHg V5 time before and during an exercise period;
(b) calculating an area above a resting PetCO2 baseline bounded by said measured PetCO2 waveform during said exercise period as an area O;
(c) calculating an area under the resting PetCO2 baseline bounded by said measured PetCO2 waveform during said exercise period as an area U;
(d) calculating the ratio of area Oarea U; and
(e) wherein if said ratio >1, PH is determined not to be present.
21. A method as in claim 20 wherein said exercise period is approximately 3 minutes.
22. A method as in claim 20 wherein said exercise is submaximal.