Pattern analysis system and method

• US 20030153817A1
• Filed: 12/30/2002
• Published: 08/14/2003
• Est. Priority Date: 12/28/2001
• Status: Active Grant

• Current Assignee: Concepta AB
• Original Assignee: Concepta AB
• Inventors: Knagenhjelm, Petter
• Granted Patent: US 7,409,373 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Method and arrangement for providing a computerized system having an interface arrangement for interfacing a data source. The data source delivering data related to motion of a person, a memory arrangement for storing said data, a processor for processing the data, an artificial neural network (ANN) using the processor, means for collecting a second set of data from the person, means for calculating one or several parameters distinctive of various features of said person, and means for feeding the parameter values to the ANN trained to recognize the various features.

Claims

1. A computerized system comprising:

an interface arrangement for interfacing a data source, said data source delivering data related to head motion of a person under influence of drugs;

a memory arrangement for storing said data;

a processor for processing said data;

an artificial neural network (ANN) using said processor;

means for collecting a second set of data from said person, said data being collected under influence of a drugs;

means for calculating one or several parameters distinctive of various features of said person; and

means for feeding said parameter values to said ANN trained to recognize said various features, comprising psychological syndromes.

2. The computerized system of claim 1, wherein said ANN further comprises a number of nodes representing sets of training data.

3. The computerized system of claim 1, further comprising means for use of Linear Predictive Coding (LPC) to analyze said parameter values fed to said ANN.

4. The computerized system of claim 1, wherein said data source is a camera.

5. The computerized system of claim 1, wherein said ANN is a Kohonen type ANN.

6. The computerized system of claim 1, being further provided with a performance test result.

7. The computerized system of claim 1, wherein said motion relates to motion of a head of said patient.

8. A method for the detection of a characteristic of a person by means of a computer which generates a report placing said person in a special category needing a treatment, said computer employing an artificial neural network (ANN) in which motion data are analyzed, said method comprising:

measuring motion data from said person;

collect other measured data from said person, under influence of a drug;

calculating one or several parameters distinctive of various characteristics;

feeding said parameter values to an ANN trained to recognize various characteristics; and

analyzing said parameter values in the neural network.

9. The method of claim 8, wherein said characteristics include at least one psychological syndrome.

10. The method of claim 8, wherein said parameters comprise at least one of:

the variance of distance;

the variance of CPT variables;

the residual signal defined as difference between the input signal and a smoothed version of the same;

an estimate of immobility duration; and

at least one additional parameter suited to detect periodicity in the one or more of the input signals.

11. The method of claim 10, wherein said variance of distance, d, is defined as:

12. The method of claim 10, wherein said variance of CPT variables such as latency defined as:

13. In a computerized system, a method for the detection of patients with psychological syndrome employing an artificial neural network (ANN) in which motion data are analyzed, said method comprising:

measuring motion data on a patient;

collecting other measured data from the patient;

calculating one or several parameters distinctive of various psychological syndromes;

feeding said parameter values to an ANN trained to recognize various psychological syndromes; and

analyzing said parameter values in the neural network.

14. The method of claim 13, wherein said parameters comprise at least one of:

the variance of distance;

the variance of CPT variables;

the residual signal defined as difference between the input signal and a smoothed version of the same;

an estimate of immobility duration; and

one or more parameters suited to detect periodicity in the one or more of the input signals.

15. The method of claim 14, wherein said variance of distance, d, is defined as:

16. The method of claim 14, wherein said variance of CPT variables is defined as:

17. The method of claim 13, wherein said ANN is trained with data collected from patients being under influence of drugs.

18. The method of claim 13, wherein said ANN comprises a number of nodes representing sets of training data.

19. The method according to claim 13, wherein the ANN is a Kohonen-map type ANN.

20. The method of claim 13, further comprising the use of linear predictive coding (LPC) to analyze the parameter values fed to the ANN.

21. The method of claim 14, wherein said parameters are used for optimal correlation between parameter distance and conceptual distance.

22. The method of claim 13, wherein, the psychological syndrome is ADHD.