EEG Signal Analysis Using Zero-Crossing Matching for Pathological Detection

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Solution Overview

Problem

Current methods for detecting pathological brain activity patterns from scalp electroencephalography are inefficient due to signal attenuation, poor spatial resolution, and noise, particularly in identifying interictal epileptiform discharges and preictal periods, which are subject-dependent and limited by linear signal analysis.

Innovation Solution

A computer-implemented method using a non-linear classification approach that computes a matching score by comparing electroencephalographic signal segments to a reference family of functions derived from zero-crossing statistical analysis, focusing on phase information to reduce artifact influence and enable generalized reference states for different subjects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If visual evaluation of scalp electroencephalogram is used, then diagnostic accuracy can be maintained, but the procedure becomes time consuming and inefficient

Engineering Contradiction:

Improvedetection efficiencyVSAvoidtime consumption

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual visual evaluation with an automated computer-based analysis system that processes electroencephalographic signals. The system uses algorithms to automatically detect pathological patterns, substituting the mechanical process of human visual inspection with an automated computational system that operates faster and more efficiently while maintaining diagnostic accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Object-affected harmful factors

If scalp electroencephalography is used, then non-invasive recording is achieved, but signal attenuation and poor spatial resolution compromise detection

Engineering Contradiction:

Improvesignal qualityVSAvoidinvasiveness

Core Design Contradiction:

Object-affected harmful factorsVSEase of operation

Solution Approach 1:

The patent transforms the electroencephalographic signal by computing the first derivative with respect to time, which enhances the detection of rapid voltage changes characteristic of pathological patterns. This parameter transformation amplifies high-frequency components and sharp transitions, compensating for the signal attenuation and poor spatial resolution inherent in scalp electroencephalography while maintaining the non-invasive advantage.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If linear signal analysis methods are used, then computational simplicity is maintained, but detection of pathological patterns is limited

Engineering Contradiction:

Improvepattern detection accuracyVSAvoidanalysis method complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces traditional linear signal analysis methods with a non-linear analysis approach that computes the first derivative of the signal and uses pattern recognition algorithms. This substitution enables more accurate detection of pathological patterns by capturing non-linear dynamics and rapid transitions that linear methods miss, while the automated computational system manages the increased complexity efficiently.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Adaptability or versatility

If subject-dependent analysis is used, then individual accuracy is improved, but generalizability to different subjects is limited

Engineering Contradiction:

Improvesubject independenceVSAvoiddetection accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent develops a universal analysis method that identifies pathological patterns through derivative-based features and automated recognition algorithms that are applicable across different subjects. The system extracts characteristic patterns that are consistent across individuals, enabling the same analytical approach to be applied universally while maintaining detection accuracy through automated pattern recognition rather than subject-specific customization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11684303B2Method for identification of pathological brain activity from scalp electroencephalogram

Publication Date: 2023.06.27 BIOSERENITY

AI summary

A computer-implemented method for detecting pathological brain activity patterns from a scalp electroencephalographic signal, the method including the steps of obtaining (A) an electroencephalographic signal as a function of multiple channels and time; identifying (C), for each channel, the zero-crossings of the electroencephalographic signal over a fixed threshold; generating a zero-crossing representation of at least a segment of the obtained electroencephalographic signal with the identified zero-crossings; obtaining (D) a reference family of real functions of time and channels from a zero-crossing statistical analysis of zero-crossing representation of pre-recorded electroencephalographic signals; calculating (E) a matching score by comparing the zero-crossing representation of a segment of the electroencephalographic signal with at least one reference function from the reference family of functions; and computing the matching score as a function of time by sliding the at least one reference function from the reference family of functions over the electroencephalographic signal.