A Sensorless Control System for an Implantable Heart Pump using a Real-time Deep Convolutional Neural Network

Real Time ◽

Sensorless Control ◽

Left Ventricular ◽

Ventricular Assist Devices ◽

Physiological Control ◽

Flow Measurements

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.

A Sensorless Control System for an Implantable Heart Pump using a Real-time Deep Convolutional Neural Network

10.36227/techrxiv.14517408.v1 ◽

2021 ◽

Author(s):

Masoud Fetanat ◽

Michael Stevens ◽

Christopher Hayward ◽

Nigel H. Lovell

Keyword(s):

Neural Network ◽

Control System ◽

Real Time ◽

Sensorless Control ◽

Left Ventricular ◽

Ventricular Assist Devices ◽

Physiological Control ◽

Flow Measurements

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.

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) ◽

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

10.1109/icaecc50550.2020.9339503 ◽

2020 ◽

Author(s):

Md. Nahidul Islam Opu ◽

Tanha Kabir Koly ◽

Annesha Das ◽

Ashim Dey

Keyword(s):

Neural Network ◽

Real Time ◽

Network Model ◽

Neural Network Model ◽

Age And Gender ◽

And Gender

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219033 ◽

2021 ◽

pp. 1-15

Author(s):

Qinyu Mei ◽

Ming Li

Keyword(s):

Neural Network ◽

Decision Making ◽

Control System ◽

Force Control ◽

Control Experiment ◽

Flexible Cable ◽

Cable Force ◽

And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.