A Sensorless Control System for an Implantable Heart Pump using a Real-time Deep Convolutional Neural Network
| Autor: | Christopher S. Hayward, Nigel H. Lovell, Michael C. Stevens, Masoud Fetanat |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Suction Adaptive control Computer Science - Artificial Intelligence Computer science 0206 medical engineering Biomedical Engineering Hemodynamics 02 engineering and technology Systems and Control (eess.SY) 030204 cardiovascular system & hematology Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine Control theory medicine FOS: Electrical engineering electronic engineering information engineering Humans Artificial neural network Models Cardiovascular Reproducibility of Results medicine.disease 020601 biomedical engineering Preload Artificial Intelligence (cs.AI) Control system Heart failure Pulmonary congestion Heart-Assist Devices Neural Networks Computer Destination therapy |
| DOI: | 10.48550/arxiv.2105.00875 |
| Popis: | Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44 %, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements. |
| Databáze: | OpenAIRE |
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