Abstract: A compartmentalized fitting system that may be used by a recipient to determine device parameters, such as threshold and/or maximum comfort levels, for a stimulating medical device is provided. The compartmentalized fitting system segregates the fitting procedure into separate sessions. For example, in an embodiment, the fitting system provides a clinician driven setup session whereby a clinician may specify parameters that are to be used in fitting the medical device. After which, the fitting system may enter a recipient driven fitting session that is used to determine one or device parameters for the stimulating medical device. Once determined, the fitting system passes control to a clinician driven approval session whereby the clinician can approve the determined set of device parameters; and, if approved store the determined device parameters. This storage may comprise loading the determined device parameters onto the stimulating medical device for subsequent use by the device.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations until a confidence threshold is reached. This confidence threshold comprises determining whether the values of each parameter value to be selected have converged on particular value with a specified confidence level. In determining whether the values have converged, one or more initial generations of the search may be excluded in computing the likelihood that the value has converged.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations until a confidence threshold is reached. This confidence threshold comprises determining whether the values of each parameter value to be selected have converged on particular value with a specified confidence level. In determining whether the values have converged, one or more initial generations of the search may be excluded in computing the likelihood that the value has converged.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise executing a tabu search wherein value sets that are determined to be bad are added to a tabu list that may be consulted to exclude tabu value sets from successive generations of the genetic algorithm.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations until a confidence threshold is reached. This confidence threshold comprises determining whether the values of each parameter value to be selected have converged on particular value with a specified confidence level. In determining whether the values have converged, one or more initial generations of the search may be excluded in computing the likelihood that the value has converged.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a recipient is described. The medical implant system is configured to provide electrical stimulation and at least one other mode of stimulation to the recipient. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations and then providing a determined set of parameter values to the medical implant system for use in providing stimulation to the recipient.

Abstract: A method, for inducing a hearing percept to a recipient of a cochlear implant, includes: building a sequence of electrical stimulation pulses having first and second inter-pulse intervals; and making stochastic the sequence including randomly selecting either the first or the second inter-pulse interval for insertion between consecutive pulses in the sequence.

Abstract: Apparatus and method for at least partially fitting a cochlear implant system to a patient is described, comprising: executing a genetic algorithm to select values for a subset of one or more parameters selected from a plurality of parameters for which values are to be selected to fit the implant, wherein said genetic algorithm is operable to generate one or more successive generations of values for said parameter subset; and determining, based on patient feedback, said values of said values for said parameter subset in each of said one or more successive generations.

Abstract: A hearing prosthesis configured for delivery of stochastic stimulation to a recipient. The hearing prosthesis comprises a sound pickup component configured to receive a sound signal having at least one pitch; a stochastic stimulation generator configured to generate a stochastic sequence of stimulation pulses having first and second inter-pulse intervals distributed stochastically throughout the sequence within controlled limits, the first inter-pulse interval based on the at least one pitch; and at least one stimulation channel to deliver the sequence of electrical stimulation pulses to the recipient.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise executing a tabu search wherein value sets that are determined to be bad are added to a tabu list that may be consulted to exclude tabu value sets from successive generations of the genetic algorithm.

Abstract: A compartmentalized fitting system that may be used by a recipient to determine device parameters, such as threshold and/or maximum comfort levels, for a stimulating medical device is provided. The compartmentalized fitting system segregates the fitting procedure into separate sessions. For example, in an embodiment, the fitting system provides a clinician driven setup session whereby a clinician may specify parameters that are to be used in fitting the medical device. After which, the fitting system may enter a recipient driven fitting session that is used to determine one or device parameters for the stimulating medical device. Once determined, the fitting system passes control to a clinician driven approval session whereby the clinician can approve the determined set of device parameters; and, if approved store the determined device parameters. This storage may comprise loading the determined device parameters onto the stimulating medical device for subsequent use by the device.

Abstract: A fitting system that may be used by a recipient to determine stimulation level parameters, such as threshold and/or maximum comfort levels, for a stimulating medical device is provided. These parameters may be for MAPs that may be used by a genetic algorithm in fitting a stimulating medical device. In obtaining these parameters, an internal component implanted in a recipient may apply stimulation to the recipient. In response, the recipient, using a user interface, may their information regarding their perception of the applied stimulation. This response may then be used to determine a stimulation level parameter that is then transmitted to the stimulation medical device for use in applying stimulation.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise using a dynamic mutation rate. This genetic algorithm may comprise generating successive generations of child populations. In executing the genetic algorithm, children may undergo mutations based on a mutation rate. This mutation rate may be dynamic and be based on the characteristics of the children in the generation. Additionally, values may be frozen during execution of the genetic algorithm if the likelihood that the value has converged on a particular value exceeds a threshold.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations until a confidence threshold is reached. This confidence threshold comprises determining whether the values of each parameter value to be selected have converged on particular value with a specified confidence level. In determining whether the values have converged, one or more initial generations of the search may be excluded in computing the likelihood that the value has converged.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a recipient is described. The medical implant system is configured to provide electrical stimulation and at least one other mode of stimulation to the recipient. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations and then providing a determined set of parameter values to the medical implant system for use in providing stimulation to the recipient.

Abstract: Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise executing a tabu search wherein value sets that are determined to be bad are added to a tabu list that may be consulted to exclude tabu value sets from successive generations of the genetic algorithm.

Abstract: A hearing prosthesis configured for delivery of stochastic stimulation to a recipient. The hearing prosthesis comprises a sound pickup component configured to receive a sound signal having at least one pitch; a stochastic stimulation generator configured to generate a stochastic sequence of stimulation pulses having first and second inter-pulse intervals distributed stochastically throughout the sequence within controlled limits, the first inter-pulse interval based on the at least one pitch; and at least one stimulation channel to deliver the sequence of electrical stimulation pulses to the recipient.

Abstract: Apparatus and method for at least partially fitting a cochlear implant system to a patient is described, comprising: executing a genetic algorithm to select values for a subset of one or more parameters selected from a plurality of parameters for which values are to be selected to fit the implant, wherein said genetic algorithm is operable to generate one or more successive generations of values for said parameter subset; and determining, based on patient feedback, said values of said values for said parameter subset in each of said one or more successive generations.