Optimal controller for cNIBP (continuous non-invasive blood pressure)

Edwards Lifesciences, based in Irvine, Califonia, is the global leader in patient-focused medical innovations for structural heart disease, as well as critical care and surgical monitoring. The Amsterdam head-quarters, BMEYE, is the non-invasive innovation center for Edwards Lifesciences. BMEYE was a spin-off from TNO that developed Nexfin, a Volume Clamp blood pressure measurement device. In 2012, BMEYE was acquired by Edwards Lifesciences and now functions as a research and development department, mainly regarding non-invasive blood pressure measurement for critical care facilities.
The main product at Edwards BMEYE is the ClearSight system, a monitoring device enabling access to advanced hemodynamic parameters by wrapping an inflatable cuff around a patient’s finger and measuring arterial blood pressure. The measurement of the arterial pressure waveform at the finger with this technology was introduced in the early 1980s. Because the method enabled for the first time a reliable measurement of the beat-to-beat blood pressure signal in a noninvasive manner, it was welcomed as a long-awaited step forward in the measurement of blood pressure. Currently, the requirement specifications of the ClearSight system, defined more than 30 years ago, have not been updated with the latest scientific and technical advances. Therefore, the existence of this project is to provide a modern and improved control system to target a broader patient population, in which the current implementation occasionally fails.
The objective of this document is to report the entire design process of developing an optimal controller for the ClearSight system. Starting by redefining the requirement specification based on available literature and company knowledge. Depending on the area of application of the blood pressure monitoring device, the specifications may vary and become stricter, henceforth, different end-users have been distinguished. After selecting the appropriate target area, their requirements are properly translated into specification values.
With regard to the controller, two modes of operation are defined: pressure loop and plethysmograph loop. First, the pressure loop is analyzed, since the components in this loop are also part of the plethysmograph loop. A system identification of the plant to control is conducted in order to derive a model in terms of control systems. This estimated model is later validated through different identification methods. After a detailed motivation of choice regarding the most suitable optimal control methods is presented, a LQG algorithm is chosen. This controller is designed and tuned in order to meet the new set of requirement specifications. Simulations in MATLAB of the optimally controlled pressure loop show that the new controller doubles the bandwidth of the current controller. Moreover, it provides stability under all tested conditions.
Next, a similar process of system identification and optimal control design is applied to the plethysmograph loop. Because this loop involves finger physiology on top of the non-linearities present at the pressure loop, it has not been rigorously analyzed in the past. Initial simulation results of the system to control go in accordance with modeling assumptions made at the company. Furthermore, the promising system identification results do establish a common knowledge for further development.
Finally, modifications of the LQG controller to become an adaptive controller are recommended for guaranteed performance. Opportunities after a successful optimal control implementation lead to higher quality waveforms more suitable for fields outside of the clinical area, such as research area and expanding the product capabilities. Ultimately, this leads to more successful measurements in a broader patient population and allow a more robust device, generating more expertise knowledge to develop future technology.