Servo-Controlled Blood Vessel Occluder Ahmed El-Gawish, Alan Chen, Hugo Loo, & Imad Mohammad Advisor: Ki Chon Background Goal: measure effect of drugs and hypertension on MYO and TGF in mediation of renal autoregulation Needed: a device that will automatically occlude and release blood vessels based on the user’s settings Requirements Pressure control intervals: 20 mmHg - simulating rapid step change in renal arterial pressure (RAP) Control over period of 200 secs – stabilizes pressure effects Mean controlled pressure range 80 – 120 mmHg – range over which the MYO and TGF mechanisms hypothetically take effect in autoregulation Components Occluder Sensor Pump Motor Software Occluder Made from 100% silicon material Proven to be physiology compatibility Size used is depends on lumen diameter Range is between 2mm to 24 mm Inject air or liquid into occluder to adjust the diaphragm/lumen diameter Air Advantages Simplicity Availability Ease of pressure control Doesn’t damage occluder Disadvantage Dangerous! Used for either short or long-term occlusion Water and saline solution Advantages Simplicity Availability Ease of pressure control Biocompatibile Disadvantage Transpires/Evaporates through silicone rubber Causes damage of silicone rubber Used for short-term occlusion (up to one hour) Glycerin Advantages No transpiration No evaporation Doesn’t cause damage occluder Biocompatible Use for long-term occlusion (excess of one hour) Sensor Blood pressure sensor is an invasive or non- invasive sensor Invasive - used or implanted directly at the measurement site (e.g., intra cardiac, blood vessel) Non-Invasive - measure systolic and diastolic blood pressure utilizing the oscillometric technique Designed to measure human blood pressure Invasive Sensors Internally placed catheter-tip sensor Catheter fluid is coupled directly to an external transducer Blood pressure is measured by observing the cavity’s changes in length with an optical signal conditioner Measuring scheme is based on white light interferometry Can be used on rats as well as humans Fiber Optic Pressure Sensors (Invasive) Non-Invasive Sensors For humans: the sensor is attached to the cuff on the wrist. Oscillometric technique is used to measure the blood pressure For rats: high cost Photoplethysmography Relatively inaccurate Imprecisely measures systolic blood pressure Over-saturation of the blood pressure signal by ambient light Difficulty in obtaining adequate blood pressure signals in dark skinned rodents Correlate poorly with direct blood pressure measurements Piezoplethysmography Similar clinical limitations to Photoplethysmography Utilizes piezoelectric ceramic crystals to record blood pressure readings More accurate than light-based/LED sensors Correlate poorly with direct blood pressure measurements. Volume Pressure Recording-VPR Independent clinical validation study in 2003 conducted at Yale University VPR correlated almost 100% with direct blood pressure measurement Utilizes a specially designed differential pressure transducer Measure six (6) blood pressure parameters simultaneously: systolic blood pressure, diastolic blood pressure, mean blood pressure, heart pulse rate, tail blood volume, and tail blood flow Non-Invasive Sensors Motor Drives the medium based on control signals Two Types Stepper Motor Servo Controlled Motor Stepper Motors Resolution is set at steps per revolution Inexpensive No need for feedback Remembers current position and knows number of steps to reach another position Uses current even when stationary Generates heat (50C - 90C) Stepper Motors (cont’d) Digitally controlled Signals cause it to settle in positions based on coil states Speed determined by controller Maintains position without signal changes Higher holding torque Stepper Motors in Action Animation source: http://www.interq.or.jp/japan/se-inoue/e_step1.htm Servo-Controlled Motors Higher resolution Smoother motion Less heat generated More expensive than stepper motors Lower Holding Torque Servo-Controlled Motors (cont’d) Faster than stepper motor Feedback determines correct positioning More complex than stepper motor Oscillates when close to the desired position due to feedback Servo-Controlled Motors (cont’d) Diagram Source: http://www.machinedesign.com/ASP/viewSelectedArticle.asp?strArticleId=58153&strSite=MDSite&Screen=CURRENTISSUE&CatID=3 On-Off Controller Logic control with only two states e.g. temperature control with a boiler that can only be turned on or off Determines whether the measurement is below a threshold If below threshold, take action Otherwise, no action is required PID Controller Proportional Integral Differential Controller Alternative to on-off control (error) = (set point) – (measurement) Set point is what you would like the value to Error would be the difference between the set point and actual value PID Controller (cont’d) Image Source: http://www.netrino.com/Publications/Glossary/PID.html P (proportional) If the proportional gain is well chosen, the time it takes to reach a new set point will be as short as possible, with overshoot (or undershoot) and oscillation minimized. Large proportional gain needed for quick response Small proportional gain needed to minimize overshoot and oscillation Achieving both at the same time may not be possible in all systems. D (differential) If the output is changing rapidly, there is a high chance of overshoot or undershoot The derivative is the change in value from the previous sample to the current sample Adding this to the proportional controller slows down the response time, but also decreases overshoot and ripple effects I (integral) If the is no change in value over time, the output may settle at the wrong value. Integral value is small in general Persistent error will cause sum to become large enough to cause a change PID Controller Summary Derivative and/or integral terms are added to proportional controllers to improve qualitative properties of a particular response. When all three terms are used together, the acronym used to describe the controller is PID. Schematic Presentation of PID Software Used to configure the PID controller Manages data acquisition from sensor DataLab 2000 References Wang, H. Siu, K., Ju, K., Moore L. C., and Chon, K. H., Identification of Transient Renal Autoregulatory Mechanism Using Time- Frequency Spectral Techniques. IEEE Transactions on Biomedical Engineering, June 2005 (52) 6:1033-1039 - - Brainstorming . . .
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