Spiking neural network (NN) architecture that uses Hebbian learning and reinforcement-learning schemes for adapting the synaptic weights is implemented in silicon and performs dynamic optimization according to hemodynamic sensor for a cardiac resynchronization therapy (CRT) device. The spiking NN architecture dynamically changes the atrioventricular (AV) delay and interventricular (VV) interval parameters according to the information provided by the intracardiac electrograms (IEGMs) and hemodynamic sensors. The spiking NN coprocessor performs the adaptive part and is controlled by a deterministic algorithm master controller. The simulated cardiac output obtained with the adaptive CRT device is 30% higher than with a nonadaptive CRT device and is likely to provide improvement in the quality of life for patients with congestive heart failure. The spiking NN architecture shows synaptic plasticity acquired during the learning process. The synaptic plasticity is manifested by a dynamic learning rate parameter that correlates patterns of hemodynamic sensor with the system outputs, i.e., the optimal AV and VV pacing intervals.

The planar thallium-201 (201Tl) myocardial perfusion scintigram is a widely used diagnostic technique for detecting and estimating the risk of coronary artery disease. Interpretation is currently based on visual scoring of myocardial defects combined with image quantitation and is known to have a significant subjective component. Neural networks learned to interpret thallium scintigrams as determined by both individual and multiple (consensus) expert ratings. Four different types of networks were explored: single-layer, two-layer backpropagation (BP), BP with weight smoothing, and two-layer radial basis function (RBF). The RBF network was found to yield the best performance (94.8% generalization by region) and compares favorably with human experts. We conclude that this network is a valuable clinical tool that can be used as a reference "diagnostic support system" to help reduce inter- and intraobserver variability. This system is now being further developed to include other variables that are expected to improve the final clinical diagnosis.