EEG-Based Machine Learning for Real-Time Ischemia Detection
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Solution Overview
Problem
Current methods for detecting reduced blood flow conditions such as ischemia and stroke during surgery lack automated, real-time monitoring capabilities, relying on human visual monitoring which is unreliable and not feasible in all settings, especially where trained neurophysiologists are not available.
Innovation Solution
A machine learning-based system that uses EEG signals to detect reduced blood flow conditions by generating feature values from EEG data, which are then input into a trained model to provide real-time alerts to medical staff, employing a data formatting engine for rapid signal processing and memory management to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human visual monitoring is used to detect reduced blood flow conditions, then medical staff can observe EEG signals, but the detection is unreliable and not feasible when trained neurophysiologists are unavailable
Solution Approach 1:
The system enables automated detection of reduced blood flow conditions using machine learning models that process EEG signals independently, eliminating the need for trained neurophysiologists to perform visual monitoring. The algorithm automatically identifies ischemic events and generates alerts, allowing the system to serve itself without requiring specialized human expertise.
Solution Approach 2:
The patent replaces the mechanical process of human visual monitoring with an automated computational system. Machine learning algorithms analyze EEG signals and detect reduced blood flow conditions, substituting the human neurological and visual processing mechanism with an electronic information processing system that operates reliably without trained personnel.
2Loss of time
If real-time detection of reduced blood flow conditions is implemented, then the time to diagnosis is reduced, but automated monitoring requires complex processing of EEG signals
Solution Approach 1:
The machine learning model is pre-trained on extensive EEG data before deployment. This preliminary training phase allows the model to learn complex patterns associated with reduced blood flow conditions, so that during real-time operation, the detection process is rapid and efficient without requiring complex processing at the moment of diagnosis.
Solution Approach 2:
The patent introduces feature extraction as an intermediary step between raw EEG signal acquisition and final diagnosis. The system extracts relevant features from EEG signals that are indicative of reduced blood flow, simplifying the input to the machine learning model and enabling real-time detection without processing the entire raw signal stream.
3Productivity
If automated machine learning detection is used, then consistent real-time monitoring is achieved, but the system requires rapid processing of EEG signals to maintain low latency
Solution Approach 1:
The patent segments the EEG signal processing into distinct stages: data acquisition, feature extraction, machine learning inference, and alert generation. This segmentation allows each component to be optimized independently, with feature extraction processing only the most relevant aspects of the signal, thereby maintaining high processing speed while achieving consistent real-time monitoring.
Solution Approach 2:
The system changes the parameter representation of EEG signals by transforming raw time-series data into extracted features that capture the essential characteristics of reduced blood flow conditions. This parameter transformation reduces the dimensionality and complexity of the data, enabling faster processing while maintaining detection accuracy and consistency.
Data Source
AI summary
This document describes machine learning techniques for detecting reduced blood flow conditions, such as ischemia and stroke, in real-time based on electroencephalography (EEG) signals. In one aspect, a method includes receiving patient data that includes a set of EEG signals generated by an EEG device measuring brain function of the patient. A set of feature values are generated for the patient using the EEG signals. The feature values are provided as input to a trained machine learning model that has been trained to detect reduced blood flow conditions of patients. An indication of whether the patient has the reduced blood flow condition is received as a machine learning output of the trained machine learning model. The indication of whether the patient has the reduced blood flow condition is provided.