Medical parameter processing system

• US 20050119866A1
• Filed: 10/21/2004
• Published: 06/02/2005
• Est. Priority Date: 11/14/2003
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation, Siemens Medical Solutions Health Services Corporation (Siemens AG)
• Original Assignee: Siemens Medical Solutions Health Services Corporation (Siemens AG)
• Inventors: Zaleski, John R.
• Application Number:
  US10/970,565
• Publication Number:
  US 20050119866A1
• Time in Patent Office: Days
• US Class Current: 702/189
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2218/08 Feature extraction

Abstract

A processing system for patient medical parameters includes a communication interface for acquiring patient parameter data comprising a medically significant signal from a patient monitoring device attached to a patient. A transform processor converts the medically significant signal into a plurality of components using a transform. A filter filters the components to exclude components based on criteria to provide filtered components. An inverse transform processor inverse-transforms the filtered components to provide a representation of the medically significant signal.

Claims

1. A processing system for patient medical parameters, comprising:

a communication interface for acquiring patient parameter data comprising a medically significant signal from a patient monitoring device attached to a patient;

a transform processor for converting said medically significant signal into a plurality of components using a transform;

a filter for filtering said components to exclude components based on criteria to provide filtered components; and

an inverse transform processor for inverse-transforming said filtered components to provide a representation of said signal.

2. A system according to claim 1, wherein said filter excludes particular components based on criteria including a statistical significance calculation and a predetermined threshold identifying at least one of, (a) a desired signal artifact and (b) a noise level.

3. A system according to claim 1, including

a display processor for presenting said signal artifacts in an image representation to a user;

and,

a threshold selection processor enabling a user to selectively exclude an artifact from said medically significant signal.

4. A system according to claim 1, including a generator for creating data representing at least one displayed user interface image supporting user selection of:

a patient;

an associated particular patient parameter type; and

an associated predetermined filtering criteria.

5. A system according to claim 1, wherein said filter excludes said components below a predetermined magnitude threshold.

6. A system according to claim 1, wherein said transform comprises at least one of:

(a) a wavelet transform, (b) an FFT, (c) a DCT, (d) signal averaging, and (e) Kalman filtering.

7. A system according to claim 1, wherein said components include time and magnitude domain representative coefficients, wherein signal magnitude and temporal location are substantially preserved through the transformation process.

8. A system according to claim 2, further comprising a data store, the data store being adapted to store at least one of, (a) all parameter data generated by a patient monitoring device, (b) selected patient parameter data generated by the patient monitoring device, and (c) components used by the inverse transform processor to provide a representation of the medically significant signal.

9. A system according to claim 8, wherein the selected parameter data generated by the patient monitoring device is used to create an assessment sheet.

10. A system according to claim 9, wherein the representation of the medically significant signal created by the inverse transform processor substantially includes at least the selected parameter data present in the assessment sheet.

11. A system according to claim 10, wherein said filter excludes particular components representing temporally adjacent patient parameter data having substantially identical values.

12. A method for processing raw data streams produced by a biomedical monitoring device, wherein the raw data streams are processed by performing the following:

inputting the raw data streams to a telemetry server having processing means capable of dividing each raw data stream into at least a first component and a second component;

forwarding the first component to a storage device capable of storing substantially all data values present in the raw data stream; and

forwarding the second component to a transform processor capable of assigning a relative significance to each data value present in the raw data stream.

13. A method according to claim 12, further comprising the transform processor generating a plurality of coefficients, each coefficient characterizing a magnitude of a data value present in the raw data stream.

14. A method according to claim 13, further comprising forwarding substantially all of the coefficients to a threshold processor for setting a threshold value that excludes a set of coefficients having a relative contribution to the data values present in the raw data stream that is less than the threshold value.

15. A method according to claim 14, further comprising the threshold processor setting a threshold value that excludes a set of coefficients representing temporally adjacent substantially repeating raw data values.

16. A method according to claim 14, further comprising the threshold processor setting a threshold value that preserves a set of coefficients representing data values present in the raw data stream that indicates a significant characteristic of the data stream.

17. A method of reducing data transmission and storage requirements in a telemetry processing system used for collecting and displaying a nonstationary event, comprising:

collecting substantially all data values present in a signal that characterizes the nonstationary event;

assigning a relative contribution value to each data value present in the signal;

excluding each data value assigned a relative contribution value having a relatively small effect on the signal, thereby creating a set of excluded data values;

including each data value assigned a relative contribution value having a relatively large effect on the signal, thereby creating a set of included data values; and

constructing an approximation of the signal using the set of included data values.

18. A method according to claim 17, further comprising excluding each relative contribution value representing a data value having a magnitude that is substantially identical to a magnitude of an adjacent data value.

19. A method according to claim 17, further comprising extracting a characteristic of the nonstationary event by identifying relative contribution values associated with the characteristic.

20. A method according to claim 19, further comprising:

storing the set of included relative contribution values in a long term data storage repository; and

discarding the set of excluded relative contribution values, thereby reducing an absolute storage requirement necessary to reconstruct the approximation of the signal.