Signal interpretation engine

• US 20040220782A1
• Filed: 05/27/2004
• Published: 11/04/2004
• Est. Priority Date: 04/24/1997
• Status: Active Grant

• Current Assignee: Daniel Reed Cook
• Original Assignee: Daniel Reed Cook
• Inventors: Cook, Daniel Reed
• Granted Patent: US 6,988,056 B2
• Time in Patent Office: Days
• US Class Current: 702/189
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/407 Evaluating the spinal cord A61B5/4896 takes precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01V 1/282 Application of seismic models, synthetic seismograms
• CPC Class Codes: G01V 1/30 Analysis G01V1/50 takes precedence
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06F 2218/12 Classification; Matching
• 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 signal interpretation engine apparatus and method are disclosed as including a computer programmed to run a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments. Each feature segment corresponds to a unique representation of the signal created by a feature operator from the signal across an epoch. An epoch corresponds to an event within a time segment. A consolidation module applies aggregators to consolidate the inner products or superposition segments into a distribution function representing an attribute over a domain reflecting a selected weight table, aggregator, and event type, corresponding to each value of the attribute.

Claims

1. A method of labeling signal data by event type comprising:

measuring and recording an event and its type;

measuring and recording signal data;

establishing a correspondence between an event and a signal data segment;

labeling signal data segments by event type.

2. The method of claim 1, wherein the establishment of correspondence between an event and a signal-data epoch is done by recording the time of the event, recording the time of the signal-data, and comparing the times in order to accurately establish which events correspond with which signal data segments.

3. The method of claim 1, wherein the events are identifiable instances of the signal data itself.

4. A method of creating useful applications of the signal interpretation engine, the method comprising:

measuring and recording events and event types;

measuring and recording signal data;

establishing a correspondence between an event and a signal data epoch;

labeling signal data epochs by event type;

using labeled signal data epochs in a learning system to generate an interpretation map;

using signal data and the interpretation map in a classification system to produce interpretations;

using the interpretations to provide a useful result.