Stroke Detection Sensor Using Neck Electrodes for Early Diagnosis
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Solution Overview
Problem
Current diagnostic techniques for stroke detection are inadequate, particularly for minor strokes, as they often rely on visible symptoms and may result in undiagnosed cases due to the inability to timely identify stroke occurrence.
Innovation Solution
A stroke detection system comprising a sensor device configured to obtain physiological data from a patient and a computing device that analyzes this data to provide a patient stroke indicator. The sensor device can include electrodes to detect brain activity data from specific brain regions, and the system can be implanted or worn externally without interfering with patient movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic techniques relying on visible symptoms are used, then the diagnostic process is simple and easy to perform, but stroke detection accuracy deteriorates especially for minor strokes
Solution Approach 1:
The diagnostic system is segmented into multiple independent components: a sensor device for obtaining physiological data, a computing device for analyzing data, and an alert system for notification. This segmentation allows the system to achieve high detection accuracy through specialized functions while maintaining ease of use through automated processing and distributed architecture.
Solution Approach 2:
The sensor device is designed to detect multiple types of physiological data including brain activity, heart activity, and other relevant signals. This multi-functionality enables a single device to improve stroke detection accuracy across various stroke types and severity levels without requiring multiple separate diagnostic tools.
2Loss of time
If continuous monitoring is implemented to improve timely detection, then stroke detection timeliness is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring of physiological data rather than truly continuous monitoring. The computing device analyzes physiological data at scheduled intervals and triggers alerts only when stroke indicators are detected, enabling timely detection while reducing energy consumption by keeping the system in low-power states between monitoring cycles.
Solution Approach 2:
The computing device automatically analyzes physiological data and determines whether stroke indicators are present without requiring constant human intervention or high-power processing. The system self-manages the monitoring workflow, activating intensive analysis only when necessary based on detected anomalies, thereby reducing overall energy consumption while maintaining detection timeliness.
3Measurement precision
If a sensor device with multiple electrodes is used to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The sensor device places electrodes at specific locations on the patient's body (such as the back of the neck or base of the skull) to optimally detect brain activity signals. This localized electrode placement provides high measurement precision for stroke detection without requiring a full scalp EEG array, thereby reducing device complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The system extracts only the essential physiological signals needed for stroke detection from the patient's body, focusing on specific brain regions and cardiac signals. By extracting and analyzing only the relevant data components rather than monitoring all physiological parameters, the system achieves high detection accuracy with a simplified sensor configuration.
4Reliability
If implantable sensor devices are used to improve detection reliability, then detection reliability is improved, but ease of operation deteriorates due to surgical requirements
Solution Approach 1:
The system offers dynamic deployment options where the sensor device can be implemented as an implantable device for high-reliability long-term monitoring, or as an external wearable device for easier deployment. This dynamic approach allows the medical community to select the appropriate implementation based on patient needs, balancing detection reliability against ease of operation and surgical intervention requirements.
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 enables early and accurate detection of strokes by analyzing physiological data, potentially reducing the risk of undiagnosed minor strokes and improving treatment outcomes by facilitating timely intervention.
Implementation Method 1
a plurality of electrodes carried by the housing, the electrodes configured to detect electrical signals corresponding to brain activity in at least the P3, Pz, and P4 brain regions of the patient
Data Source
AI summary
A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.