Download Physiological Closed-Loop Control For Smart Medical Systems Nicholas Candelino , Paneed Jalili ,

Graduate
Category:
Degree Level:
Abstract ID#
Engineering and Technology
PhD
1330
Physiological Closed-Loop Control For Smart Medical Systems
1
Candelino ,
2
Jalili ,
Nicholas
Paneed
1
3
Nader Jalili and Dagmar Sternad
1 Piezoactive Systems Laboratory, Department of Mechanical Engineering
2 Behavioral Neuroscience Department, College of Science
3 Departments of Biology, Electrical and Computer Engineering, and Physics
Northeastern University, Boston MA
Clinician and Controller:
Controller Works as An Asset to Clinical Staff
Designed for Reliability and Fault Tolerance
Utilizing Robust and Adaptive Approaches
Clinicians can Always Override the Controller
Alert Clinicians to Unusual/Unsafe Situations
𝑹
Desired Clinical Results:
Hypnosis and Analgesia
Neuro-blockade
Maintain Normal SO2
Avoid Hypo/Hyperglycemia
Maintain Normal Heart Rhythm
Desired Result
Σ
Patient/Therapy Model:
Inter/Intrapatient variability
Actuator Dynamics
Pharmacokinetics
Pharmacodynamics
Time Delays and Possible Interruptions
𝒀
𝑷
𝑪
Patient Health
Clinician & Controller
Qualitative Health Indicators:
Consciousness and Cognition
Comfort/Discomfort
Pain
Mobility
Fatigue
Patient & Therapy Delivery
𝑯
Sensed Patient Health
Additional Factors:
Surgical Disturbances
Unexpected/Excessive Exercise
Movement
Measurement Noise
Unmodeled Drug Interactions
Sensing Patient Health:
Measurable States Only
• EEG or BIS
• SpO2, Heart Rate, Temperature, etc.
Health/Signal Correlation
Sensor Dynamics
Abstract
Challenges
Recent advances in medical sensing devices have enabled the accurate measurement of
physiological signals; however, the long-standing patient-clinician-device paradigm still
remains largely unaffected and/or in its infancy stage because most modern medical devices
are not capable of performing self-regulation of applied therapeutic actions. As a result, the
effectiveness of these machines heavily depend on several external factors; such as the ones
mentioned below:
The design of controllers for physiological systems is among the most demanding of
any application.
•
•
•
•
Manual adjustment provides limited resolution, precision, and range
The quality of regulation depends on the frequency of visits from clinical staff
The guidelines used to perform adjustments may not be well suited to a particular patient
Uncertainties in the assessment of a patient’s condition may obscure the conversion from
intended results to the manual adjustment of settings
By designing self-regulating, self-adapted behaviors into therapeutic delivery systems, clinical
treatments can be applied with a higher degree of confidence. While such instruments may or
may not contain integral sensing equipment, they should be equipped with on-board signal
processing and control systems. These systems will leverage modern processing techniques to
make real-time adjustments based on data from multiple simultaneously acquired
physiological signals. Proper tuning and adaptation may result in instruments that are robust
against a variety of possible conditions in the patient population. Instead of being a decoupled
combination of blind devices, these devices will, in turn, become more intelligent and capable
of making decisions guided by the principles of physiology and closed-loop control to perform
personalized treatment or drug administration as intended by clinicians.
The dynamics of a physiological system can vary across the general population and
even within a single patient. These systems may have several unknowns, only a small
number of measureable states, and many potential disturbances. Any device interacting
directly with patients may directly influence their well being and need to achieve the
highest levels of safety during operation and protections against faults such as missing
signals.
Method
Investigate the patient, therapy, and sensing models currently being used to find an
optimal control ready representation, applying reduction techniques if necessary.
Further, these models can be used to help determine performance specifications.
Survey methods of Robust and Adaptive Control theory, both currently used in PCLC
and applied in other industries, to find optimal control approach.
In most cases, it will be beneficial to apply control techniques which incorporate
adaptive or variable gains (see Figure 1) to maintain system stability for the largest
possible population.
Control
Inputs
Goal
𝑪
This research is aiming to devise methods for controller development and system level
design which fully take into account the many safety and operational considerations
necessary in patient-in-the-loop systems. Ultimately, this may lead to resilient, smart medical
systems which provide an enhanced clinical experience, a higher quality of care, and an
increase in promising patient outcomes.
Key Benefits
• Suppress effects of interpatient variability
• Allow clinicians to concentrate on higher
level tasks
• Reduce sensitivity to uncertainty and
external disturbances
• Avoid dosage under/overshoot
• Reduction of cost due to minimization of
unnecessary drug consumption and
shorter time spent by patients in
postoperative care units
• Allow each clinician to treat more patients
Σ
(To Plant)
Control Action
Estimator
Gain Modifications
Adaptation
Law
+
Σ
–
(From Sensors)
Figure 1: Example of Variable Gain Adaptive Control Scheme
Conclusions
Once proper design and evaluation methodologies for PCLC systems are established,
the path to replacing our outdated pumps, injections, clinical guidelines, and alarms will
be cleared. Smart Medical Systems will likely be adopted wherever feasible, increasing
the potential for achieving optimal patient outcomes and perhaps more advanced care in
hospitals, during transportation, and in the home.