EEG Headset Stroke Detection Using Frequency Power Ratios
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Solution Overview
Problem
Current diagnostic methods for stroke, such as CT and MRI, are limited in availability and time-consuming, leading to delayed treatment and misdiagnosis of stroke versus stroke mimics, particularly in rural areas, and there is a lack of a stand-alone, accurate point-of-care system to differentiate between ischemic and hemorrhagic strokes for timely administration of tPA.
Innovation Solution
A non-invasive EEG-based headset with 8 electrodes, using the 10-20 international system for placement, captures neural electrical signals, processes them through a client-side signal processing engine to compute frequency-dependent ratios, and distinguishes between stroke and stroke mimics, as well as ischemic and hemorrhagic strokes, providing rapid diagnosis and treatment guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT scan is used for stroke diagnosis, then stroke detection is possible, but the scan may not show infarct within 6-8 hours and delays treatment
Solution Approach 1:
The patent applies preliminary action by implementing a pre-screening tool using clinical assessment algorithms and mobile applications that can identify stroke symptoms before formal imaging. This allows early detection and triage of stroke patients, enabling them to reach specialized centers within the critical time window when CT/MRI can confirm diagnosis and initiate treatment.
2Measurement precision
If MRI scan is used for stroke diagnosis, then accurate detection is possible, but the procedure time and transit time delay treatment
Solution Approach 1:
The patent implements preliminary action by using a mobile pre-screening application that can assess stroke probability before patient arrival at the hospital. This preliminary assessment prepares the medical team in advance, so that when the patient reaches the imaging department, the MRI can be scheduled and performed without delays caused by initial assessment and decision-making.
Solution Approach 2:
The patent segments the diagnosis process into distinct phases: (1) pre-hospital screening using mobile applications and clinical algorithms, (2) rapid triage upon arrival, and (3) confirmatory imaging. This segmentation allows each phase to be optimized independently, with the pre-screening handling initial assessment to reduce the time burden on the imaging phase.
3Ease of operation
If patient is sent to general physician for initial assessment, then accessibility is improved, but diagnosis time is delayed
Solution Approach 1:
The patent applies feedback by implementing decision support systems and algorithms that provide real-time guidance to general physicians during patient assessment. The system analyzes patient symptoms and clinical data, provides feedback on stroke probability, and guides the physician on whether to refer to a stroke center, thereby reducing unnecessary referrals and expediting appropriate cases.
Solution Approach 2:
The patent introduces an intermediary layer in the form of mobile health applications and telemedicine platforms that connect general physicians with stroke specialists. This intermediary enables remote consultation and rapid referral decision-making, allowing general physicians to access specialized expertise without physical patient transfer until necessary.
4Reliability
If brain imaging is required before tPA administration, then safety is improved, but treatment time is delayed
Solution Approach 1:
The patent applies preliminary action by implementing pre-screening protocols and algorithms that assess stroke probability and identify high-risk patients before they reach the imaging department. This preliminary risk stratification allows hospitals to prepare imaging resources in advance and prioritize these patients, reducing the time from arrival to imaging and subsequent tPA administration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves sensitivity and specificity of over 85% in differentiating stroke from mimics and distinguishing between stroke types, reducing setup and test time to under 5 minutes, and enabling timely administration of tPA to ischemic stroke patients.
Implementation Method 1
Electroencephalographic (EEG) signals serve as an important source of information when it comes to brain functionality
Implementation Method 2
divide said amplified signals into epochs on which Fast Fourier Transform is performed in order to obtain transformed signals
Data Source
AI summary
Systems and methods for detection of stroke and its types, comprising: electrodes, on an EEG headset (101), placed to record EEG signals; a client-side signal processing engine configured to: compute power for each of signals; segregate processed signal, from each of said electrodes, into five baskets, by processing signals from each of said electrodes such that there is a Delta basket, a Theta basket, an Alpha basket, a Beta basket extract features from a frequency component of said transformed signals in order to obtain stroke ratios; receive, as a first output, a first set of processed signals with power ratings for determination of a stroke incident as a function of power rating ratios; receive, as a second output, a second set of processed signals with relative powers for determination of a type of stroke as a function of said first relative power (RDP) and said second relative power (RAP).