Method and apparatus for extracting low SNR transient signals from noise
First Claim
1. A method of extracting low SNR signals from noise comprising the following steps:
- a) dynamically identifying, via a competitive neural network, various transient response types embedded within a recorded ensemble of composite signals;
b) decomposing the response types;
c) constructing a parametric synthesis model that emulates a single-sweep signal generation mechanism.
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Abstract
A method and apparatus for processing a composite signal generated by a transient signal generation mechanism to extract a repetitive low SNR transient signal, such as an evoked potential (EP) appearing in an electroencephalogram (EEG) generated in response to sensory stimulation, by: (a) dynamically identifying, via a learning process, the major transient signal types in the composite signal; (b) decomposing the identified major transient signal types into their respective constituent components; (c) synthesizing a parametric model emulating the transient signal generation mechanism; and (d) utilizing the model and the constituent components to identify and extract the low SNR transient signal from the composite signal.
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25 Claims
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1. A method of extracting low SNR signals from noise comprising the following steps:
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a) dynamically identifying, via a competitive neural network, various transient response types embedded within a recorded ensemble of composite signals;
b) decomposing the response types;
c) constructing a parametric synthesis model that emulates a single-sweep signal generation mechanism. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 22, 23, 24, 25)
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12. An apparatus for extracting low SNR signals from noise comprising:
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a competitive neural network transient response identifier;
a response type decomposition unit;
a parametric synthesis model that emulates a single-sweep signal generation mechanism. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification