In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using neural networks. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The parameter configurations may include one or more of a variety of parameters, such as electrode configurations defining electrode combinations and polarities for an electrode set implanted in a patient. The electrode set may be carried by one or more implanted leads that are electrically coupled to the neurostimulator. In operation, the programming device executes a parameter configuration search algorithm to guide the clinician in the selection of parameter configurations. The search algorithm relies on a neural network that identifies potential optimum parameter configurations.

Selection of neurostimulator parameter configurations using bayesian networks

A selection of parameter configurations for a neurostimulator using decision trees may be employed by a programming device to allow a clinician or other user to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The programming device executes a parameter configuration search algorithm to guide the clinician in selection of parameter configurations. The search algorithm relies on a decision tree to identify optimum parameter configurations. A decision tree is useful in classifying observations in a data set based upon one or more attributes or fields within the data. The data set includes parameter configurations matched with observed ratings of efficacy on patients of a similar indication. The learned attribute, on which classification occurs, will be the optimum parameter configuration for a set of rated configurations used to produce the classification. The decision trees may be especially useful in identifying electrode configurations.

Selection of neurostimulator parameter configurations using decision trees

In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using genetic algorithms. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. In operation, the programming device executes an electrode configuration search algorithm to guide the clinician in the selection of electrode configurations. The search algorithm relies on a genetic algorithms to identify potential optimum electrode configurations within an electrode set. The genetic algorithms provide guidance in the electrode configuration selections process, interactively guiding the clinician by suggesting the configurations that are most likely to be efficacious given the results of tests already performed during an evaluation session.

Selection of neurostimulator parameter configurations using genetic algorithms

The disclosure describes an implantable stimulation system that guides programming with a therapeutic tree. All possible stimulation parameters are arranged on the therapeutic tree, with each level of the therapeutic tree containing a different stimulation parameter type. Each level includes nodes that are connected to nodes of adjacent levels. A program path is created by moving through nodes of lower levels. The stimulation parameter types are arranged so that coarse adjustments occur at higher levels of the tree and fine adjustments occur at lower levels of the tree. The nodes of the program path define the stimulation parameters of the delivered stimulation therapy. The user may provide information such as efficacy input and/or medication dosage information to the system for identifying the most efficacious program path in treating pain of the patient. Additionally or alternatively, efficacy feedback may be received from physiological parameter sensors.

Tree-based electrical stimulator programming for pain therapy

A workstation-user interface for evaluating computer assisted diagnosis (CAD) methods for digital mammography is disclosed. Implementation of such an interface enables multiple, large-size images to be handled at high speeds. Furthermore, controls such as contrast, pan, and zoom, and tools such as reporting forms, case information, and analysis of results are included. The software and hardware used to develop such a workstation and interface were based on Sun platforms and the Unix operating system. The software is user friendly, and comparable to standard mammography film reading in terms of display layout and speed. The software, as designed, will work on entry-level workstations as well as high-end workstations with specialized hardware, thus being usable in an educational, training, or clinical environment for annotation purposes using CAD techniques as well as primary diagnosis.

/pdf/workstation-interface-for-use-in-digital-mammography-and-ageafmvez6.pdf

Workstation interface for use in digital mammography and associated methods

The present invention comprises a method for the detection and segmentation of bright images within a digital image using wavelets. One example of such a bright image is microcalcifications within a mammogram. Multiresolution analysis may be used to detect and segment the possible microcalcification areas by combining Bayes classifiers. By analyzing the time-frequency characteristics of clustered microcalcifications, the inventors first choose the optimized wavelet for the detection of microcalcifications. A wavelet packet analysis is then used to detect different size microcalcifications. An adaptive method of choosing the threshold for detection by using a one-dimensional wavelet transform to analyze the PDF of the images at different scales is used. Then, a scheme to detect different size microcalcifications in different scale wavelet packet transformed images is developed. The processing results show that the inventors' methods are quite effective on a broad sample of mammograms in detecting microcalcifications even at low contrast. These segmentation and detection methods will prove useful in other applications, such as other medical imaging infrared applications, forward-looking infrared radar (FLIR), and other technologies.

Method for automatic detection of targets within a digital image

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Method for automatic detection of targets within a digital image