[Signal Conversion and Isolation Processing Technology Used in the Cerebrovascular Stroke Detector]

Area of Science:

• Biomedical Engineering
• Medical Devices
• Cardiovascular Diagnostics

Background:

• Cerebrovascular stroke detection relies on accurate measurement of carotid artery hemodynamics.
• Existing methods may lack comprehensive signal processing for integrated blood flow and pressure analysis.

Purpose of the Study:

• To develop and validate a cerebrovascular stroke detector capable of measuring carotid artery blood flow velocity and blood pressure.
• To implement advanced signal processing techniques for feature extraction and analysis of sensor outputs.

Main Methods:

• Utilized Doppler and pressure sensors for real-time measurement of carotid artery parameters.
• Employed a variety of signal conversion and isolation processing techniques.
• Developed methods for feature extraction from sensor output signals.

Main Results:

• Obtained effective signal output waveforms for evaluating the cerebrovascular hemodynamics index (CVHI).
• Generated sound signal outputs reflecting changes in blood flow velocity and blood pressure.
• Successfully met the functional requirements for a cerebrovascular stroke detector application.

Conclusions:

• The developed detector effectively integrates Doppler and pressure sensing with advanced signal processing.
• The system provides valuable outputs for assessing cerebrovascular hemodynamics and stroke risk.
• The technology demonstrates potential for improved non-invasive cerebrovascular diagnostics.