Abstract: In an illustrative example, a method includes sensing at least a portion of a representation of a convolutional low-density parity-check (CLDPC) codeword stored at a memory of a data storage device. The method further includes receiving the portion of the representation of the CLDPC codeword at a controller of the data storage device. The method further includes performing one or more management operations associated with the memory based on an estimated number of errors of the portion of the representation of the CLDPC codeword.

Abstract: In an illustrative example, a method includes sensing at least a portion of a representation of a convolutional low-density parity-check (CLDPC) codeword stored at a memory of a data storage device. The method further includes receiving the portion of the representation of the CLDPC codeword at a controller of the data storage device. The method further includes performing one or more management operations associated with the memory based on an estimated number of errors of the portion of the representation of the CLDPC codeword.

Abstract: A data storage device includes a shaping engine and a compression engine. The shaping engine is configured to shape first data to generate second data. The compression engine is configured to compress the second data to generate third data.

Abstract: A method and system for regulating the operation of a cardiac pacemaker or defibrillator are disclosed. A processor receives signals of both an implanted hemodynamic sensor and intracardiac electrograms and digitizes them. The digitized signal of the hemodynamic sensor is used to prevent inappropriate cardiac stimulation and erroneous cardiac detection. The hemodynamic signal is also used to define arrhythmias.

Abstract: A system and method for optimizing cardiac resynchronization therapy and visualizing cardiac pacing intervals. The system comprises at least one hemodynamic sensor; at least one electrode; a learning module; a micro controller; and at least one graphical interface for showing at least one of a PRV vs. PLV diagram and a responder curve.

Abstract: A closed loop Deep Brain Stimulation (DBS) system constituted of: a physiological sensor; a multi-electrode DBS lead; an adaptive control system in communication with the physiological sensor; and an implantable pulse generator (IPG) responsive to the adaptive control system, the adaptive control system comprising a learning module operable to learn to find the optimal stimulation parameters, classify and associate patient conditions responsive to the physiological sensor with optimal stimulation parameters in a plurality of patient conditions. The adaptive DBS device control system learns to deliver the optimal stimulation parameters based on Watkins and Dayan Q learning recursive formula, the closed loop adaptive DBS control system thus finds the optimal stimulation parameters online.

Abstract: An adaptive dual chamber pacemaker and/or cardioverter defibrillator for delivering ventricular stimulation to the heart correlated with hemodynamic performance of the heart, including a hemodynamic sensor for monitoring the hemodynamic performance of the heart, an atrial electrode and a ventricular electrode for sensing ventricular and atrial signals, and a learning module having a spiking neural network processor for learning to associate the ventricular-atrial intervals sensed by the electrodes with the hemodynamic performance sensed by the hemodynamic sensor, calculating ventricular-atrial intervals, replacing the ventricular-atrial intervals calculated from the sensed ventricular and atrial signals with the learned associated ventricular-atrial intervals, and causing delivery according to the learned associated ventricular-atrial intervals of a ventricular stimulation to the heart during atrial fibrillation episodes.

Abstract: A method of detecting and classifying cardiac arrhythmias, comprising: receiving a hemodynamic wave signal from at least one hemodynamic sensor by a processor; receiving a cardiac electrical wave signal from a cardiac stimulation device by the processor; integrating, by the processor, the received hemodynamic wave signal and the received cardiac electrical wave signal; determining if a heart arrhythmia is present via examination of the regularity of the hemodynamic signal; and if a heart arrhythmia is determined, classifying the arrhythmia according to a time correlation between the hemodynamic signal and the electrical signal.

Abstract: An adaptive dual chamber pacemaker and/or cardioverter defibrillator for delivering ventricular stimulation to the heart correlated with hemodynamic performance of the heart, including a hemodynamic sensor for monitoring the hemodynamic performance of the heart, an atrial electrode and a ventricular electrode for sensing ventricular and atrial signals, and a learning module having a spiking neural network processor for learning to associate the ventricular-atrial intervals sensed by the electrodes with the hemodynamic performance sensed by the hemodynamic sensor, calculating ventricular-atrial intervals, replacing the ventricular-atrial intervals calculated from the sensed ventricular and atrial signals with the learned associated ventricular-atrial intervals, and causing delivery according to the learned associated ventricular-atrial intervals of a ventricular stimulation to the heart during atrial fibrillation episodes.

Abstract: A cardiac pacemaker control system constituted of: a means for receiving input from a hemodynamic sensor; an adaptive control system in communication with the means for receiving input from the hemodynamic sensor; and an interface arranged to provide cardiac stimulation responsive to the adaptive control system; the adaptive control system comprising a learning module operative to converge to patient specific cardiac pacing stimulation timing using a machine learning scheme in cooperation with a probabilistic replacement scheme, the probabilistic replacement scheme arranged to replace inputs from the hemodynamic sensor with online calculated values.

Abstract: An adaptive cardiac resynchronization therapy system delivers biventricular stimulation to the heart with dynamic AV delay and VV interval. The stimulation is modified continuously in correlation with the hemodynamic performance of the heart. The system uses a spiking neural network comprising spike controller (42) that learns to associate the VA interval based on hemodynamic sensor temporal patterns. The associated VA interval replaces the sensed atrial event signal during atrial fibrillation episodes.

Abstract: An adaptive CRT control system that achieves optimal AV delay and VV pacing intervals associated with temporal patterns of stroke volumes that represent internally the heart conditions is disclosed.

Abstract: A system and method for optimizing cardiac resynchronization therapy and visualizing cardiac pacing intervals. The system comprises at least one hemodynamic sensor; at least one electrode; a learning module; a micro controller; and at least one graphical interface for showing at least one of a PRV vs. PLV diagram and a responder curve.

Abstract: A closed loop Deep Brain Stimulation (DBS) system constituted of: a physiological sensor; a multi-electrode DBS lead; an adaptive control system in communication with the physiological sensor; and an implantable pulse generator (IPG) responsive to the adaptive control system, the adaptive control system comprising a learning module operable to learn to find the optimal stimulation parameters, classify and associate patient conditions responsive to the physiological sensor with optimal stimulation parameters in a plurality of patient conditions. The adaptive DBS device control system learns to deliver the optimal stimulation parameters based on Watkins and Dayan Q learning recursive formula, the closed loop adaptive DBS control system thus finds the optimal stimulation parameters online.

Abstract: A cardiac pacemaker control system constituted of: a means for receiving input from a hemodynamic sensor; an adaptive control system in communication with the means for receiving input from the hemodynamic sensor; and an interface arranged to provide cardiac stimulation responsive to the adaptive control system; the adaptive control system comprising a learning module operative to converge to patient specific cardiac pacing stimulation timing using a machine learning scheme in cooperation with a probabilistic replacement scheme, the probabilistic replacement scheme arranged to replace inputs from the hemodynamic sensor with online calculated values.

Abstract: A development system for a cardiac pacemaker control system, the development system comprising: a cardiac pacemaker prototype including an adaptive control system; and a heart simulator in communication with the cardiac pacemaker prototype comprising: a means for receiving pacing stimulations output from the cardiac pacemaker prototype; and a hemodynamic sensor model responsive to the received pacing stimulations, the hemodynamic sensor model being operable to output a signal simulating a hemodynamic sensor in a plurality of heart states, the hemodynamic sensor model being further operative to output the signal simulating the hemodynamic sensor in the plurality of heart states in a plurality of heart conditions.

Abstract: An adaptive feed-back controlled system for regulating a physiological function of a heart in which a hemodynamic sensor continuously monitors the physiological performance of the heart. Three implanted electrodes sense and pace the right atrial, right ventricle and left ventricle. A learning neural network module receives and processes information for the electrodes (18) and sensors (22), and is controlled by a deterministic module for limiting said learning module. A pulse generator (16), is also controlled by the deterministic module, and stimulates both the heart and the vagus (20).

Abstract: A method and system for regulating the operation of a cardiac pacemaker or defibrillator are disclosed. A processor receives signals of both an implanted hemodynamic sensor and intracardiac electrograms and digitizes them. The digitized signal of the hemodynamic sensor is used to prevent inappropriate cardiac stimulation and erroneous cardiac detection. The hemodynamic signal is also used to define arrhythmias.

Abstract: An adaptive cardiac resynchronization therapy system delivers biventricular stimulation to the heart with dynamic AV delay and VV interval. The stimulation is modified continuously in correlation with the hemodynamic performance of the heart. The system uses a spiking neural network comprising spike controller (42) that learns to associate the VA interval based on hemodynamic sensor temporal patterns. The associated VA interval replaces the sensed atrial event signal during atrial fibrillation episodes.

Abstract: An adaptive CRT control system that achieves optimal AV delay and VV pacing intervals associated with temporal patterns of stroke volumes that represent internally the heart conditions is disclosed.