A new rule-based control system for rotary ventricular assist devices (rVADs) is proposed. The control system is comprised of two modules: a suction detector and a rule-based controller. The suction detector can classify pump flow patterns, based on a discriminant analysis (DA) model that combines several indices derived from the pump flow signal, to make a decision about the pump status. The indices considered in this approach are frequency, time, and time-frequency-domain indices. These indices are combined in a DA decision system to generate a suction alarm.
The suction detector performance was assessed using experimental data and in simulations. Experimental results comprise predictive discriminant analysis (classification accuracy: 100% specificity, 93% sensitivity on training set and 97% specificity, 86% sensitivity on test set) of the detector and descriptive discriminant analysis (explained variance) of the DA model. To perform the simulation studies, the suction detector was coupled to a cardiovascular-pump model that included a suction model. Simulations were carried out to access the detector performance, under different physiological conditions, i.e., by varying preload and the contractility state of the left ventricle. To verify its robustness to noise, simulations were carried out to verify how the accuracy of the detector is affected when increasing levels of noise are added to the pump flow signal.
The rule-based controller uses fuzzy logic to combine the discriminant scores from the DA model to automatically adjust the pump speed. The effects on controller performance of symmetric or asymmetric membership output sets and the dimension of the rule base were evaluated in simulations. The same parameter changes, i.e., preload and contractility, were used to assess the control system performance under different physiologic scenarios in simulations. The proposed control system is capable of automatically adjusting pump speed, providing pump flow according to the patient’s level of activity, while sustaining adequate perfusion pressures and avoiding suction. In addition, the control system performance was not adversely affected by noise until SNR was less than 20dB, which is a higher noise level than is commonly encountered in flow sensors used clinically for this type of application.
|School:||University of Pittsburgh|
|School Location:||United States -- Pennsylvania|
|Source:||DAI-B 70/04, Dissertation Abstracts International|
|Subjects:||Biomedical engineering, Electrical engineering|
|Keywords:||Pattern recognition, Rotary blood pumps, Suction detection, Ventricular assist devices|
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