Implantable neurostimulation systems have proven therapeutic in a wide variety of diseases and disorders. Pacemakers and Implantable Cardiac Defibrillators (ICDs) have proven highly effective in the treatment of a number of cardiac conditions (e.g., arrhythmias). Spinal Cord Stimulation (SCS) systems have long been accepted as a therapeutic modality for the treatment of chronic pain syndromes, and the application of tissue stimulation has begun to expand to additional applications such as angina pectoralis and incontinence. Deep Brain Stimulation (DBS) has also been applied therapeutically for well over a decade for the treatment of refractory chronic pain syndromes, and DBS has also recently been applied in additional areas such as movement disorders and epilepsy. Further, in recent investigations, Peripheral Nerve Stimulation (PNS) systems have demonstrated efficacy in the treatment of chronic pain syndromes and incontinence, and a number of additional applications are currently under investigation. Furthermore, Functional Electrical Stimulation (FES) systems, such as the Freehand system by NeuroControl (Cleveland, Ohio), have been applied to restore some functionality to paralyzed extremities in spinal cord injury patients.
These implantable neurostimulation systems typically include one or more electrode carrying stimulation leads, which are implanted at the desired stimulation site, and a neurostimulator (e.g., an implantable pulse generator (IPG)) implanted remotely from the stimulation site, but coupled either directly to the stimulation lead(s) or indirectly to the stimulation lead(s) via a lead extension. The neurostimulation system may further comprise an external control device to remotely instruct the neurostimulator to generate electrical stimulation pulses in accordance with selected stimulation parameters.
Electrical stimulation energy may be delivered from the neurostimulator to the electrodes in the form of an electrical pulsed waveform. Thus, stimulation energy may be controllably delivered to the electrodes to stimulate neural tissue. The combination of electrodes used to deliver electrical pulses to the targeted tissue constitutes an electrode combination, with the electrodes capable of being selectively programmed to act as anodes (positive), cathodes (negative), or left off (zero). In other words, an electrode combination represents the polarity being positive, negative, or zero. Other parameters that may be controlled or varied include the amplitude, width, and rate of the electrical pulses provided through the electrode array. Each electrode combination, along with the electrical pulse parameters, can be referred to as a “stimulation parameter set.”
With some neurostimulation systems, and in particular, those with independently controlled current or voltage sources, the distribution of the current to the electrodes (including the case of the neurostimulator, which may act as an electrode) may be varied such that the current is supplied via numerous different electrode configurations. In different configurations, the electrodes may provide current or voltage in different relative percentages of positive and negative current or voltage to create different electrical current distributions (i.e., fractionalized electrode combinations).
As briefly discussed above, an external control device can be used to instruct the neurostimulator to generate electrical stimulation pulses in accordance with the selected stimulation parameters. Typically, the stimulation parameters programmed into the neurostimulator can be adjusted by manipulating controls on the external control device to modify the electrical stimulation provided by the neurostimulator system to the patient. Thus, in accordance with the stimulation parameters programmed by the external control device, electrical pulses can be delivered from the neurostimulator to the stimulation electrode(s) to stimulate or activate a volume of tissue in accordance with a set of stimulation parameters and provide the desired efficacious therapy to the patient. The best stimulus parameter set will typically be one that delivers appropriate stimulation energy to the volume of tissue that is targeted for therapeutic benefit (e.g., treatment of pain), while minimizing the volume of non-target tissue that is stimulated.
However, the number of electrodes available, combined with the ability to generate a variety of complex stimulation pulses, presents a huge selection of stimulation parameter sets to the clinician or patient. For example, if the neurostimulation system to be programmed has an array of sixteen electrodes, millions of stimulation parameter sets may be available for programming into the neurostimulation system. Today, neurostimulation system may have up to thirty-two electrodes, thereby exponentially increasing the number of stimulation parameters sets available for programming.
To facilitate such selection, a patient care professional (e.g., a clinician, field engineer, sales representative, etc.) generally programs the neurostimulator through a computerized programming system. This programming system can be a self-contained hardware/software system, or can be defined predominantly by software running on a standard personal computer (PC), personal data assistant (PDA), or other computerized device. The PC or custom hardware may actively control the characteristics of the electrical stimulation generated by the neurostimulator to allow the optimum stimulation parameters to be determined based on patient feedback or other means and to subsequently program the neurostimulator with the optimum stimulation parameter set or sets, which will typically be those that stimulate all of the target tissue in order to provide the therapeutic benefit, yet minimizes the volume of non-target tissue that is stimulated. The computerized programming system may be operated by a clinician attending the patient in several scenarios.
Thus, in the application of electrical neurostimulation therapy, the goal is to identify a pertinent paradigm of stimulation that properly stimulates neural tissue. Significantly, it may sometimes be desirable to estimate or predict the stimulation effects of electrical energy applied, or to be applied, to neural tissue adjacent to electrodes based on an estimation of the membrane response (e.g. transmembrane voltage potentials) of one or more neurons induced by the actually applied or potentially applied electrical energy. For example, given a specific set of stimulation parameters, it may be desired to predict a region of stimulation within the neural tissue of a patient based on an estimation of the neuronal response. As another example, when transitioning between electrode configurations, it may be desirable to adjust the intensity of the electrical stimulation energy based on an estimation of the transmembrane voltage potentials.
Estimating the transmembrane voltage potential of a neuron in response to an applied electric field generally includes two steps: (1) computing the extracellular electric field in response to the conveyance of electrical energy from a specific electrode configuration; and (2) computing the transmembrane voltage potential of the neuron in response to the generated electric field. The calculation of the extracellular electric field is straightforward and can be done analytically or numerically. Calculating the transmembrane voltage potential, however, is more dynamic and highly non-linear in both time and space. Several neural models are now available for estimating the induced neural response to an extracellular electric field, which range from simplified analytical approximations to computationally intensive network models. In commercial neurostimulator development, the trade off between the model efficiency and computational efficiency has to always be considered.
In one computationally efficient method, a linear cable model (passive model in that the membrane conductance is constant) assumes that neuronal activation occurs if the induced depolarization is greater than a critical transmembrane voltage potential, which is commonly assumed to be fixed when, in fact, it is a function of pulsewidth. Thus, passive neural models have been considered accurate only for long pulses (steady-state polarization). In another computationally efficient method, an activating function (2nd spatial derivative of the external electrical field), which describes the sources that are driving the neuron, is used to estimate the membrane response of a neuron to an externally applied electric field. The activating function, however, does not include any information about the neuron, and thus provides an accurate description of the sources, but not the neural response to the sources.
Thus, neither of these methods can be used to accurately predict the neural response to an externally applied electric field. Another method has been proposed that combines the use of passive neural models and activating functions. In particular, as described in E. N. Warman, W. M. Grill, and D. Durand, Modeling the Effects of Electric Fields on Nerve Fibers: Determination of Excitation Threshold, IEEE Trans. On Biomed Engr. Vol. 39, No. 12, 1992, a total equivalent driving function has been proposed for estimating the transmembrane voltage potentials induced in passive cable models of neuronal elements to an externally applied electric field. This method takes into account the activating function, pulse width of the electrical stimulation energy, and passive properties of neuron models, thereby eliminating errors associated with the use of the activating function alone. However, due largely to the computational complexity involved in solving for partial differential equations to obtain the passive step response of the neuron to an intracellular electrical current stimulation, the application of a total equivalent driving function to estimate a neural response to an external applied electric field is presently limited.
There, thus, remains a need for a computationally efficient technique for estimating the stimulation effect (e.g., transmembrane voltage potential of a neuron) in the presence of an externally conveyed electric field.
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USPTO Backgrounds