Microprocessor system for the analysis of physiologic and financial datasets

• US 20030158466A1
• Filed: 05/17/2002
• Published: 08/21/2003
• Est. Priority Date: 01/27/1997
• Status: Active Grant

• Current Assignee: Eric N Lynn, Lawrence A. Lynn
• Original Assignee: Eric N Lynn, Lawrence A. Lynn
• Inventors: Lynn, Eric N., Lynn, Lawrence A.
• Granted Patent: US 7,758,503 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system and method for organization and analysis of complex and dynamically interactive time series is disclosed. One example comprises a processor based system for relational analysis of physiologic signals for providing early recognition of catastrophic and pathologic events such as pathophysiologic divergence. The processor is programmed to identify pathophysiologic divergence of at least one of first and second physiologic parameters in relationship to the other and to output an indication of the divergence. An object-based method of iterative relational processing waveform fragments in the time domain is described wherein each more complex waveform object inherits the characteristics of the waveform objects from which it is derived. The first physiologic parameter can be the amplitude and frequency of the variation in chest wall impedance or nasal pressure and the second parameter can be a measure or indication of the arterial oxygen saturation.

Claims

1. ) A microprocessor system for evaluation of a patient, the system comprising:

a monitor having a plurality of sensors for positioning adjacent said patient and a processor programmed to;

a) produce a first timed waveform based on a first physiologic parameter of a patient,

b) produce a second timed waveform based on a second physiologic parameter which is generally subordinate to said first physiologic parameter, so that said second parameter normally changes in response to changes in said first parameter,

c) identify pathophysiologic divergence of at least one of said first and second physiologic parameters in relationship to the other of said physiologic parameters,

d) outputting an indication of said divergence.

2. ) The system, as in claim 1, wherein the processor is further programmed to:

a) calculate an index of said divergence and,

b) provide an indication based on said index.

3. ) The system of claim 1, wherein said first parameter comprises at least one of an indication and measure of the magnitude of timed ventilation of a patient.

4. ) The system of claim 1, wherein said second parameter comprises a measure of arterial oxygenation.

5. ) The system of claim 3, wherein said first parameter is at least one of the amplitude and frequency of the variation in chest wall impedance.

6. ) The system of claim 3, wherein said first parameter is at least one of the amplitude and frequency of the variation in nasal pressure.

7. ) The system of claim 3, wherein said first parameter is at least one of the amplitude and frequency of the variation of at least one of the tidal carbon dioxide.

8. ) The system of claim 3, wherein the first parameter is measure of the timed volume of at least one gaseous component of ventilation.

9. ) A method of monitoring a patient comprising:

a) monitoring a patient to produce a first timed waveform of a first physiologic parameter and a second timed waveform of a second physiologic parameter, the second physiologic parameter being physiologically subordinate to said first physiologic parameter,

b) identifying a pattern indicative of divergence of at least one of said waveforms in relation to a physiologically expected pattern of the other of said waveforms,

c) outputting an indication of said divergence.

10. ) The system of claim 9, wherein said first timed waveform is defined by a time interval of greater than about 5-20 minutes.

11. ) The system of claim 9 wherein said first and second time series are physiologic time series derived from airflow and pulse oximetry.

12. ) The system of claim 9 wherein said processor comprises a primary processor, said system further including a secondary processor and at least one of a diagnostic and treatment device, said primary processor being connectable to said secondary processor, said secondary processor being programmed to control at least one of said diagnostic and treatment device, said secondary processor being programmed to respond to the output of said primary processor.

13. ) The system of claim 12 wherein said primary processor is programmed to adjust the said program of said secondary processor.

14. ) The system of claim 12 wherein said treatment device is an airflow delivery system controlled by a secondary processor, said secondary processor being programmed to recognize hypopneas, said primary processor adjusting said program of said secondary processor based on said identifying.

15. ) The system of claim 12 wherein said treatment device is an automatic defibrillator

16. ) The system of claim 12 wherein said secondary processor is mounted with at least one of said treatment and diagnostic device, said primary processor being detachable from said connection with said secondary processor.

17. ) The system of claim 12 wherein said primary processor is hospital patient monitor capable of monitoring and analyzing a plurality of different patient related signals including electrocardiographic signals.

18. ) The system of claim 12 wherein said primary processor is a polysomnography monitor capable of monitoring a plurality of different signals including encephalographic signals.