Intelligent sensing of EEG signals

Khalid J. Siddiqui; Leslie E. Collins; Dennis Fitzpatrick; Shelton Hendricks; D. Robert Hay; Ching Y. Suen

doi:10.1117/12.23562

1 November 1990 Intelligent sensing of EEG signals

Khalid J. Siddiqui, Leslie E. Collins, Dennis Fitzpatrick, Shelton Hendricks, D. Robert Hay, Ching Y. Suen

Proceedings Volume 1349, Applications of Digital Image Processing XIII; (1990) https://doi.org/10.1117/12.23562
Event: 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1990, San Diego, CA, United States

Abstract

Although physician observation is usually the most sensitive method for diagnosing and monitoring a patient''s medical condition human observation cannot be conducted continuously and consistently. It can be helpful therefore to employ specialized automated techniques for the continuous reliable and noninvasive monitoring of those parameters useful for the enhancement of physicians'' diagnostic capabilities. Signal processing systems are among the most powerful of those techniques currently available for noninvasively examining the internal structure of living biological systems. Nonetheless the capability of these systems can be substantially enhanced if supplemented with automated classification and interpretation precedures. An intelligent EEG signal sensing and interpretation system using typical signal processing techniques supplemented with heuristics and identification techniques has been designed. The system is comprised of five major components namely: the fact gathering system the knowledge/rule base the knowledge organization/learning phase the inference engine and the expert/user interface. The fact gathering system collects raw waveforms preprocesses these for noise elimination and extracts the pertinent information from the waveforms. The knowledge/rule base is an information and knowledge bank wherein the appropriate knowledge parameters useful for the decision making process are stored. The knowledge organization/learning phase structures the knowledge In the order determined by the association among pattern classes and trains the Inference engine. The structure of the inference engine is based on a hierarchical pattern classifier which categorizes the unknown signals using a layered decision making strategy

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Khalid J. Siddiqui, Leslie E. Collins, Dennis Fitzpatrick, Shelton Hendricks, D. Robert Hay, and Ching Y. Suen "Intelligent sensing of EEG signals", Proc. SPIE 1349, Applications of Digital Image Processing XIII, (1 November 1990); https://doi.org/10.1117/12.23562

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KEYWORDS

Electroencephalography

Signal processing

Classification systems

Diagnostics

Intelligence systems

Digital image processing

Pattern recognition

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