Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers

Summary Implantable cardiac pacemaker is a standard medical device to treat heart rhythm disorders. In this paper, a new adaptive backstepping controller is developed to enhance the performance of dual‐sensor pacemakers for regulating the heart rate based on radial basis function neural networks. The robust design of adaptive backstepping controller using Lyapunov functions allows to guarantee the stability and performance of the rate‐adaptive pacing system for accurately accomplishing the heart rate regulation at different preset or desired values. The developed control system has been successfully validated using 12 cases of the preset heart rates for 4 patients during 3 body activities, namely, at rest, walking, and jogging. The resulting root mean square error and maximum error are less than 0.9 and 1.7%, respectively. Moreover, the comparative results of this study showed that the performance of developed backstepping controller is superior to other pacemaker controllers in the previous studies. Therefore, it is potentially valid to be applied in dual‐sensor cardiac pacemakers for the clinical use.