early stroke identification using microwave helmet
DESCRIPTION
This presentation gives a brief description about the stroke finder helmet which was developed recently by scientists. It describes about the main components in the stroke finder helmet and its advantages over the current diagnosis systems that we have now a days.TRANSCRIPT
EARLY STROKE IDENTIFICATION USING MICRO WAVE
HELMET
BY, GUIDED BY,
CIJU VARGHESE ANJALI. R
S7 EC B ASST. PROF.
R.NO 10 EC DEPT.
1
INTRODUCTION
Stroke is the No.3 cause of death. (#1 – Heart Disease, #2 – Cancer)
5 million people/year die and another 5 million are permanently disabled.
Every 3.1 minutes someone dies of a stroke.
Time is a crucial factor.
For neurologists “time is brain”.
Limitations of current diagnosis.
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WHAT IS STROKE ?????
Brain tissues are damaged from a sudden loss of blood flow, resulting in a loss of neurological function
Types: Ischemic stroke (85%) is blood flow is
blocked to the brain.Hemorrhagic Stroke (15%) is bleeding
occurs from vessels within the brain.
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CURE FOR STROKE
tPA (Tissue Plasminogen Activator)
is a clot-busting drug
It dissolves blood clots obstructing blood flow to the brain.
Medical Surgery
4
WHY MICROWAVE DIAGNOSIS?
Microwave property of non-ionising radiations.
Zero side effects to body.
Relatively cheap,compact,portable technology.
Vast research and development in Microwave Tomography(MWT).
Exploits dielectric variations in brain.5
DIELECTRICS IN HUMAN BRAIN
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STROKE FINDER HELMET
Consists of:
A helmet-like antenna system which is placed on the patient’s head.
A microwave unit plus a computer for equipment control, data collection.
Signal Processing.
7
ANTENNA SYSTEM
10 triangular patch antennas.
Power input is 1mW.
Frequency Range 0.3-3.0GHz
A liquid(water) bag is placed between antenna and skull.
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WORKING OF ANTENNA SYSTEM
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SIGNAL PROCESSING
Data from each channel is pre-processed.
The total output signal power is made equal.
Data is transformed by an algorithm.
Full reconstruction of dielectric parameters algorithm is used.
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RECONSTRUCTED IMAGE
PERFORMANCE ASSESSMENT
Specificity v/s Sensitivity is plotted.
Area under curve(AUC) should be between 0.5 and 1.
Better performance to detect ICH v/s healthy(AUC=0.88).
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FUTURE SCOPES
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CONCLUSION
15
REFERENCES