Patent ID: 12246179

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG.1is a system10for stimulating tissue of the central nervous system. The system includes a lead12placed in a desired position in contact with central nervous system tissue. In the illustrated embodiment, the lead12is implanted in a region of the brain, such as the thalamus, subthalamus, or globus pallidus for the purpose of deep brain stimulation. However, it should be understood, the lead12could be implanted in, on, or near the spinal cord; or in, on, or near a peripheral nerve (sensory or motor) for the purpose of selective stimulation to achieve a therapeutic purpose.

The distal end of the lead12carries one or more electrodes14to apply electrical pulses to the targeted tissue region. The electrical pulses are supplied by a pulse generator16coupled to the lead12.

In the illustrated embodiment, the pulse generator16is implanted in a suitable location remote from the lead12, e.g., in the shoulder region. It should be appreciated, however, that the pulse generator16could be placed in other regions of the body or externally.

When implanted, the case of the pulse generator can serve as a reference or return electrode. Alternatively, the lead12can include a reference or return electrode (comprising a bi-polar arrangement), or a separate reference or return electrode can be implanted or attached elsewhere on the body (comprising a mono-polar arrangement).

The pulse generator16includes an on-board, programmable microprocessor18, which carries embedded code. The code expresses pre-programmed rules or algorithms under which a desired electrical stimulation waveform pattern or train is generated and distributed to the electrode(s)14on the lead12. According to these programmed rules, the pulse generator16directs the prescribed stimulation waveform patterns or trains through the lead12to the electrode(s)14, which serve to selectively stimulate the targeted tissue region. The code is preprogrammed by a clinician to achieve the particular physiologic response desired.

In the illustrated embodiment, an on-board battery20supplies power to the microprocessor18. Currently, batteries20must be replaced every 1 to 9 years, depending on the stimulation parameters needed to treat a disorder. When the battery life ends, the replacement of batteries requires another invasive surgical procedure to gain access to the implanted pulse generator. As will be described, the system10makes possible, among its several benefits, an increase in battery life.

The stimulation waveform pattern or train generated by the pulse generator differs from convention pulse patterns or trains in that the waveform comprises repeating non-regular (i.e., not constant) pulse patterns or trains, in which the interval between electrical pulses (the inter-pulse intervals or IPI) changes or varies over time. Examples of these repeating non-regular pulse patterns or trains are shown inFIGS.3to5. Compared to conventional pulse trains having regular (i.e., constant) inter-pulse intervals (as shown inFIG.2), the non-regular (i.e., not constant) pulse patterns or trains provide a lower average frequency for a given pulse pattern or train, where the average frequency for a given pulse train (expressed in hertz or Hz) is defined as the sum of the inter-pulse intervals for the pulse train in seconds (ΣIPI) divided by the number of pulses (n) in the given pulse train, or (ΣIPI)/n. A lower average frequency makes possible a reduction in the intensity of side effects, as well as an increase in the dynamic range between the onset of the desired clinical effect(s) and side effects, thereby increasing the clinical efficacy and reducing sensitivity to the position of the electrode(s). A lower average frequency brought about by a non-regular pulse pattern or train also leads to a decrease in power consumption, thereby prolonging battery life and reducing battery size.

The repeating non-regular (i.e., not constant) pulse patterns or trains can take a variety of different forms. For example, as will be described in greater detail later, the inter-pulse intervals can be linearly cyclically ramped over time in non-regular temporal patterns (growing larger and/or smaller or a combination of each over time); or be periodically embedded in non-regular temporal patterns comprising clusters or groups of multiple pulses (called n-lets), wherein n is two or more. For example, when n=2, the n-let can be called a doublet; when n=3, the n-let can be called a triplet; when n=4, the n-let can be called a quadlet; and so on. The repeating non-regular pulse patterns or trains can comprise combinations of single pulses (called singlets) spaced apart by varying non-regular inter-pulse intervals and n-lets interspersed among the singlets, the n-lets themselves being spaced apart by varying non-regular inter-pulse intervals both between adjacent n-lets and between the n pulses embedded in the n-let. If desired, the non-regularity of the pulse pattern or train can be accompanied by concomitant changes in waveform and/or amplitude, and/or duration in each pulse pattern or train or in successive pulse patterns or trains.

Each pulse comprising a singlet or imbedded in an n-let in a given train comprises a waveform that can be monophasic, biphasic, or multiphasic. Each waveform possesses a given amplitude (expressed, e.g., in amperes) that can, by way of example, range from 10 pa (E−6) to 10 ma (E−3). The amplitude of a given phase in a waveform can be the same or differ among the phases. Each waveform also possesses a duration (expressed, e.g., in seconds) that can, by way of example, range from 10 ρs (E−6) to 2 ms (E−3). The duration of the phases in a given waveform can likewise be the same or different. It is emphasized that all numerical values expressed herein are given by way of example only. They can be varied, increased or decreased, according to the clinical objectives.

When applied in deep brain stimulation, it is believed that repeating stimulation patterns or trains applied with non-regular inter-pulse intervals can regularize the output of disordered neuronal firing, to thereby prevent the generation and propagation of bursting activity with a lower average stimulation frequency than required with conventional constant frequency trains, i.e., with a lower average frequency than about 100 Hz.

FIG.3shows a representative example of a repeating non-regular pulse pattern or train in which the inter-pulse intervals are linearly cyclically ramped over time. As shown inFIG.3, the pulse pattern or train includes singlet pulses (singlets) spaced apart by progressively increasing inter-pulse intervals providing a decrease in frequency over time, e.g., having an initial instantaneous frequency of 140 Hz, decreasing with doubling inter-pulse intervals, to a final instantaneous frequency of 40 Hz. The inter-pulse intervals can vary within a specified range selected based upon clinical objections, e.g., not to exceed 25 ms, or not to exceed 100 ms, or not to exceed 200 ms, to take into account burst responses and subsequent disruption of thalamic fidelity). The non-regular pulse trains repeat themselves for a clinically appropriate period of time. As shown inFIG.3, the first pulse train comprises progressively increasing inter-pulse intervals from smallest to largest, followed immediately by another essentially identical second pulse train comprising progressively increasing inter-pulse intervals from smallest to largest, followed immediately by an essentially identical third pulse train, and so on. Therefore, between successive pulse trains, there is an instantaneous change from the largest inter-pulse interval (at the end of one train) to the smallest inter-pulse interval (at the beginning of the next successive train). The train shown inFIG.3has an average frequency of 85 Hz and is highly non-regular, with a coefficient of variation (CV) of about 0.5. As is demonstrated in the following Example (Batch 3), the increased efficiency of the pulse train shown inFIG.3(due to the lower average frequency) also can provide greater efficacy, as compared to a constant 100 Hz pulse pattern.

The train shown inFIG.3exploits the dynamics of burst generation in thalamic neurons. The early high frequency phase of the train masks intrinsic activity in subthalamic nucleus (STN) neurons, and the inter-pulse interval increases reduce the average frequency. A family of trains can be provided by varying the initial frequency, final frequency, and rate of change within the train, with the objective to prevent thalamic bursting with a lower average stimulation frequency than required with constant frequency trains.

FIGS.4and5show other representative examples of repeating non-regular pulse patterns or trains. The pulse trains inFIGS.4and5comprise within, a single pulse train, a combination of single pulses (singlets) and embedded multiple pulse groups (n-lets), with non-regular inter-pulse intervals between singlets and n-lets, as well as non-regular inter-pulse intervals within the n-lets themselves. The non-regular pulse trains repeat themselves for a clinically appropriate period of time.

The non-regular pulse train can be characterized as comprising one or more singlets spaced apart by a minimum inter-pulse singlet interval and one or more n-lets comprising, for each n-let, two or more pulses spaced apart by an inter-pulse interval (called the “n-let inter-pulse interval”) that is less than the minimum singlet inter-pulse interval. The n-let inter-pulse interval can itself vary within the train, as can the interval between successive n-lets or a successive n-lets and singlets. The non-regular pulse trains comprising singlets and n-lets repeat themselves for a clinically appropriate period of time.

InFIG.4, each pulse train comprises four singlets in succession (with non-regular inter-pulse intervals there between); followed by four doublets in succession (with non-regular inter-doublet pulse intervals there between and non-regular inter-pulse intervals within each n-let); followed by a singlet, three doublets, and a singlet (with non-regular inter-pulse intervals there between and non-regular inter-pulse intervals within each n-let). The temporal pattern of this pulse train repeats itself in succession for a clinically appropriate period of time. The non-regular temporal pulse pattern shown inFIG.4has an average frequency of 67.82 Hz without loss of efficacy, as is demonstrated in the following Example, Batch 17.

InFIG.5, each pulse train comprises four singlets in succession (with non-regular inter-pulse intervals there between); followed by three doublets in succession (with non-regular inter-doublet pulse intervals there between and non-regular inter-pulse intervals within each n-let). The temporal pattern of this pulse train repeats itself in succession for a clinically appropriate period of time. The non-regular temporal pulse pattern shown inFIG.5has an average frequency of 87.62 Hz without loss of efficacy, as is demonstrated in the following Example, Batch 18.

The following Example illustrates a representative methodology for developing and identifying candidate non-regular stimulation trains as shown inFIGS.3to5that achieve comparable or better efficacy at a lower average frequency (i.e., more efficiency) than constant inter-pulse interval trains.

EXAMPLE

Computational models of thalamic DBS (McIntyre et al. 2004, Birdno, 2009) and subthalamic DBS (Rubin and Terman, 2004) can be used with genetic-algorithm-based optimization (Davis, 1991) (GA) to design non-regular stimulation patterns or trains that produce desired relief of symptoms with a lower average stimulation frequency than regular, high-rate stimulation. McIntyre et al. 2004, Birdno, 2009; Rubin and Terman, 2004; and Davis, 1991 are incorporated herein by reference.

In the GA implementation, the stimulus train (pattern) is the chromosome of the organism, and each gene in the chromosome is the IPI between two successive pulses in the train. The implementation can start, e.g., with trains of 21 pulses (20 genes) yielding a train length of−400 ms (at average frequency of 50 Hz), and the 6 s trains required for stimulation are built by serial concatenation of 15 identical pulse trains. The process can start with an initial population of, e.g., 50 organisms, constituted of random IPI's drawn from a uniform distribution. At each step (generation) of the GA, the fitness of each pulse train is evaluated using either the TC or basal ganglia network model (identified above) and calculating a cost function, C. From each generation, the 10 best stimulus trains (lowest C) are selected, to be carried forward to the next generation. They will also be combined (mated) and random variations (mutations) introduced into the 40 offspring, yielding 50 trains in each generation. This process assures that the best stimulation trains (traits) are carried through to the next generation, while avoiding local minima (i.e., mating and mutations preserve genetic diversity). See Grefenstette 1986. The GA continues through successive generations until the median and minimum values of the cost function reach a plateau, and this will yield candidate trains.

The objective is to find patterns of non-constant inter-pulse interval deep brain stimulation trains that provide advantageous results, as defined by low frequency and low error rate. An error function is desirably created that assigns the output of each temporal pattern of stimulation a specific error fraction (E) based on how the voltage output of the thalamic cells correspond to the timing of the input stimulus. Using this error fraction, a cost function (C) is desirably created to minimize both frequency and error fraction, according to representative equation C=W*E+K*f, where C is the cost, E is the error fraction, f is the average frequency of the temporal pattern of stimulation, W is an appropriate weighting factor for the error function, and K is an appropriate weighting factor for the frequency. The weighting factors W and K allow quantitative differentiation between efficacy (E) and efficiency (f) to generate patterns of non-constant inter-pulse interval deep brain stimulation trains that provide advantageous results with lower average frequencies, compared to conventional constant frequency pulse trains.

With this cost function, the voltage output of several candidate temporal patterns of stimulation can be evaluated and the cost calculated. Temporal patterns of stimulation with a low cost can then be used to create new temporal patterns of similar features in an attempt to achieve even lower costs. In this way, new temporal patterns of stimulation can be “bred” for a set number of generations and the best temporal patterns of stimulation of each batch recorded.

Several batches of the genetic algorithm yields useful results in that they achieve lower costs than the corresponding constant frequency DBS waveforms. Some batches can be run in an attempt to find especially low frequency temporal patterns of stimulation, by changing the cost function to weight frequency more heavily, or vice versa (i.e., by changing W and/or K). These batches can also yield lower cost results than the constant-frequency waveforms.

By way of example, a total of 14 batches of the genetic algorithm were run and evaluated with various cost functions and modified initial parameters.

Before the trials were run, a baseline was established by running constant-frequency patterns of stimulation through the model and analyzing the associated error fractions (FIG.6). As can be seen fromFIG.6, the healthy condition produced a low error fraction of 0.1 while the Parkinsonian condition without DBS yielded a higher error fraction of 0.5. From these results, constant high-frequency patterns of stimulation ranging from 100-200 Hz gave near perfect results. Novel non-constant temporal patterns of stimulation would then be considered advantageous if they showed error fractions very close to 0.1 with average frequencies less than 100-200 Hz.

The first set of batches was run by minimizing only the error fraction (E). Thus, the associated cost function was simply C=E. The results are summarized according to average frequency and error fraction (Example Table 1). The associated inter-pulse intervals (IPI's) can be seen inFIG.7. Batch 3 outputted an error fraction 0.054. Another interesting feature is that the IPI's in Batch 3 gradually increased until about 40 msec, and then repeated itself. This provides support that ramp trains are advantageous. The trace shown inFIG.3generally incorporates the temporal features of Batch 3.

The remaining batches yielded error fractions higher than 0.1 and were no better than the 150 Hz constant-frequency case.

Example Table 1: Error Fraction Only, C = E#Average FrequencyError FractionIPI Length3127.50.0545495.620.162395113.60.13913694.640.132267101.60.14231

Because many batches were yielding error fractions above 0.1 (healthy condition), and only a small window of error fraction less than the 150 Hz DBS case would be useful, a new cost function was constructed to minimize an alternate feature of the temporal patterns of stimulation; namely, frequency. This new cost function weighted the error fraction and frequency, yielding the equation C=1000*E+F, where C is cost, E is error fraction, and F is the average frequency of the waveform in Hz, W=1000, and K=1.

In order to establish a new baseline cost, the constant-frequency patterns of stimulation were evaluated again according to the new cost function (FIG.8). As can be seen from the graph, the healthy condition reported a cost of 90.65 and the Parkinson case with no DBS yielded 505.50. The best constant-frequency pattern of stimulation with the new cost function was the 100 Hz case with a cost of 231.11. This new cost' function allowed for a wider range of solutions, because a temporal pattern of stimulation would be considered useful if it had a cost less than 231.11 but presumably higher than 90.65.

The results of the new cost function can be seen in Example Table 2 and the IPI's visualized inFIG.9. The best results were seen in batches 15 and 18, which had the lowest costs. Batch 18 is interesting in that it also exhibits a ramp-like pattern of increasing interpulse intervals. It shows a steadily falling IPI, followed by a sudden rise, and then a quick fall, rise, and fall-almost as if it consists of 3 smaller ramps. The trace shown inFIG.5generally incorporates the temporal features of Batch 18. Batch 15 also performed very well, but its qualitative features are more difficult to discern.

ExampleTable 2: Cost Function, C = 1000 * E + FAverageIPI#FrequencyLengthError FractionCost994.74340.124218.813132.9120.087219.41598.00170.098196.01881.28100.116197.31984.70200.116201.2

The advantage of low frequency was emphasized with a new cost function, which weighted frequency more heavily, C=1000*E+2*F. Because the frequency of DBS does not affect the healthy condition or the PD with no DBS, these baseline costs stayed the same at 90.65 and 505.50, respectively. The 100 Hz was again the best constant-frequency temporal pattern of stimulation, with a cost of 331.11. The following temporal patterns of stimulation, then, were considered useful if they had low frequencies and costs less than 331.11 and greater than 90.65.

The results of the revised cost function can be seen in Example Table 3 and the IPI's visualized inFIG.10. Of the resulting batches, batch 17 proved most interesting because of its very low average frequency of 67.82 Hz. Even with such a low frequency, it managed to prove better than the 100 Hz condition with a reduction in cost of about 10. The waveform of batch 17 is interesting in that it consists of a ramp pattern of decreasing IPI in the first 100 msec, followed by continual shift between large IPI and small IPI. The qualitative feature of quickly changing between large and small IPI's may prove advantageous. The trace shown inFIG.4generally incorporates the temporal features of Batch 17.

ExampleTable 3:Revised Cost Function, Cost = 1000 * E + 2 * FAverageIPI#FrequencyLengthError FractionCost1684.92470.239323.81767.82200.253321.12079.25100.236315.42177.15200.269346.6

The most interesting temporal patterns of stimulation in this Example are from batches 15, 17, and 18. Batch 15 produced a temporal pattern of stimulation with an average frequency of 98 Hz with an error fraction as low as 0.098. Thus, it outperformed the 100 Hz constant-frequency case by managing to lower the error even further at roughly the same frequency. Still, the qualitatively useful features of batch 15 are difficult to discern. Batch 17 was also appealing because of its very low frequency of 67.82. This low frequency was gained at the cost of increased error at 0.253, but it may nonetheless be useful if emphasis is placed on maintaining low frequency DBS. The qualitative features of batch 17 indicated at first a ramp followed by a continual switching between low and high IPI's. Lastly, batch 18 stood somewhere in the middle with a fairly low frequency of 87.62 and low error fraction of 0.116, only marginally higher than the healthy condition of 0.1. The dominant qualitative feature of batch 18's waveform is that it too shows a ramp nature in that the IPI initially steadily falls, then quickly rises, falls, and then rises. The rapid changing between high and low IPI of batch 17 can be envisioned as a set of steep ramps.

A comparison of Batch 17 (FIG.4) and Batch 18 (FIG.5) demonstrates how the balance between efficacy (E) and efficiency (f) in non-regular temporal patterns of stimulation can be purposefully tailored to meet clinical objectives. The systems and methodologies discussed allow changing the cost function by weighting efficacy (E) or frequency (f) more heavily (i.e., by changing W and/or K), while still yielding temporal patterns of stimulation with lower cost results than the constant-frequency waveforms. Comparing Batch 17 with Batch 18, one sees that the error fraction (E) (i.e., the efficacy of the temporal pattern) of Batch 17 (0.253) is greater than the error fraction (E) (i.e., the efficacy of the temporal pattern) of Batch 18 (0.116). However, one can also see that the efficiency (i.e., the average frequency) of Batch 17 (67.82 Hz) is lower than the efficiency (i.e., the average frequency) of Batch 18 (81.28 Hz). Through different in terms of efficacy and efficiency, both Batch 17 and Batch 18 have costs better than constant-frequency temporal patterns.

The non-regular temporal patterns of stimulation generated and disclosed above therefore make possible achieving at least the same or equivalent (and expectedly better) clinical efficacy at a lower average frequency compared to conventional constant-frequency temporal patterns. The lower average frequencies of the non-regular temporal stimulation patterns make possible increases in efficiency and expand the therapeutic window of amplitudes that can be applied to achieve the desired result before side effects are encountered.

DBS is a well-established therapy for treatment of movement disorders, but the lack of understanding of mechanisms of action has limited full development and optimization of this treatment. Previous studies have focused on DBS-induced increases or decreases in neuronal firing rates in the basal ganglia and thalamus. However, recent data suggest that changes in neuronal firing patterns may be at least as important as changes in firing rates.

The above described systems and methodologies make it possible to determine the effects of the temporal pattern of DBS on simulated and measured neuronal activity, as well as motor symptoms in both animals and humans. The methodologies make possible the qualitative and quantitative determination of the temporal features of low frequency stimulation trains that preserve efficacy.

The systems and methodologies described herein provide robust insight into the effects of the temporal patterns of DBS, and thereby illuminate the mechanisms of action. Exploiting this understanding, new temporal patterns of stimulation can be developed, using model-based optimization, and tested, with the objective and expectation to increase DBS' efficacy and increase DBS efficiency by reducing DBS side effects.

The present teachings provide non-regular stimulation patterns or trains that may create a range of motor effects from exacerbation of symptoms to relief of symptoms. The non-regular stimulation patterns or trains described herein and their testing according to the methodology described herein will facilitate the selection of optimal surgical targets as well as treatments for new disorders. The non-regular stimulation patterns or trains described herein make possible improved outcomes of DBS by reducing side effects and prolonging battery life.

Another important consideration to improve efficiency and/or efficacy of the stimulation applied is to effectively utilize the waveform generated by the applicable pulse generator. Waveform shapes may be modified to provide different elements of control to the stimulation. Exemplary embodiments of a method of electing an applicable waveform shape to stimulation is described in U.S. patent application Ser. No. 13/118,081, entitled “Waveform Shapes for Treating Neurological Disorders Optimized for Energy Efficiency,” which is hereby incorporated by reference. By way of a non-limiting example, a global optimization algorithm, such as a genetic algorithm, may be utilized to determine a waveform shape to be applied by the pulse generator16. Such waveform shape or shapes may be selected to improve the efficiency, efficacy or both of the pulse generator16.

The waveform shape applied, however, may often be limited by system and hardware constraints of the pulse generator16applying the stimulation, e.g., the constraints or limitations of the memory, power source and/or microprocessor of the pulse generator. There may be occasion that a pre-defined pulse generator16may be desired to be used in the method described above for finding patterns of non-constant inter-pulse interval deep brain stimulation trains that are either incapable of applying the desired waveform shape or the application of such waveform shape may not meet the specified goals of efficiency and/or efficacy.

In such situations, the waveform shape may be limited by the pulse generator16applying such. For example, the microprocessor positioned within the pulse generator16may have limitations as to the waveform shape of the electrical stimulation it is able to apply, such as the microprocessor having limited memory, limited functionality and the like. Similarly, the power source of the pulse generator16may limit the waveform shapes that may be applied by the specific pulse generator16efficiently and/or effectively. Also, the memory of the pulse generator16may limit the waveform shapes that may be applied by the specific pulse generator16efficiently and/or effectively.

It may be desired, therefore, that the waveform shape may be chosen to be the most beneficial to the pulse generator16being utilized. A beneficial waveform shape may be one that is easy for the pulse generator16to generate and/or one that is efficient to generate by the applicable pulse generator16. The actual waveform shape may change based upon the type, specifications, parameters, or functionality of the pulse generator16utilized. Therefore, it may be desired for different pulse generators16to apply different waveform shapes to the electrical stimulation. Further, it may be desirable that the pulse generator16apply different waveform shapes to improve efficacy, efficiency or both of the stimulation. This is of particular importance to extend battery life of the applicable pulse generator16, i.e., the life of the power source. As noted above, extending the life of the pulse generator16may allow a patient to undergo fewer pulse generator replacement surgeries, which saves money, avoids complications from surgery and reduces discomfort to the patient.

In some embodiments, the waveform shape applied by the pulse generator16may be generally rectangular seeFIG.11. This rectangular waveform shape may be optimal for some pulse generators16, but may not be optimal for other pulse generators16, i.e., it may take more energy than desired to apply such waveform shape. For those pulse generators16that such rectangular waveform shape is not optimal a different shaped waveform shape may be chosen and applied by the pulse generator16. By way of a non-limiting example, an optimization method-such as through use of an algorithm, including, without limitation a genetic algorithm as described in U.S. patent application Ser. No. 13/118,081 (now U.S. Pat. No. 9,089,708, which is incorporated herein by reference)—may be utilized to determine the optimal waveform shape based upon the pulse generator16applying the stimulation. In addition or alternatively, a trial and error approach may be utilized. Further still, the waveform shape may be specific to the pulse generator16utilized, i.e., what waveform shape can the pulse generator generate efficiently. Such considerations may include the limitations of the memory, power source or microprocessor of the pulse generator16. Regardless of the approach, once the applicable waveform shape is identified, the applicable pulse generator16may be modified—such as through programming such—to apply the predetermined waveform shape.

By way of a non-limiting example, a clinician may elect to utilize a rising ramp waveform shape such as shown inFIG.12. The clinician may modify the pulse generator16to apply such waveform shape or utilize a pulse generator16that efficiently utilizes such waveform shape—the clinician or a technician may program the pulse generator16to output the predefined waveform shape. Such programming may be accomplished directly on the pulse generator16or by operatively coupling the pulse generator16to a computer source (e.g., a desktop computer, laptop or tablet). The clinician may evaluate the applied waveform shape and may either elect to utilize such in applying the therapy or chose another waveform shape. Examples of potential waveform shapes are shown inFIGS.11-16. It should be understood that the waveform shapes shown and described herein are merely exemplary and the present teachings are not limited to those waveform shapes shown and described herein. Any appropriate waveform shape may be utilized without departing from the present teachings.

As identified above, the waveform shape may be limited by the pulse generator16being utilized. In such embodiments, an easier to generate waveform shape—such as a capacitive shaped waveform shown inFIG.16—may be the most efficient waveform shape for the particular pulse generator16utilized. By way of a non-limiting example, such capacitive shaped waveform may have an efficiency of approximately 99.5%. In these embodiments, the pulse generator16utilized drives the election of the waveform shape. The applicable waveform shape may be chosen so that it is easy for the pulse generator16to produce, efficiently created by the pulse generator16, or a combination of such. Further still, a particular waveform shape may be chosen so that a smaller or more simplified pulse generator may be utilized to apply the stimulation.

A smaller pulse generator16, especially one that is implanted into a patient is beneficial for the patient—it may take up less space within the patient, be tolerated better by the patient, require less cutting to insert or any combination of these factors. Further, a more simplified pulse generator may be beneficial to increase the life span that such pulse generator is able to operatively function. This may benefit patients, especially those undergoing long term therapies by reducing the number of times that the pulse generator may need to be replaced, which reduces the number of surgeries required over the life of the patient.

Regardless of the method of determining the applicable waveform shape, once selected by the clinician and programmed into the pulse generator16, the method described above to elect the appropriate temporal pattern of stimulation, i.e., the appropriate non-regular stimulation patterns or trains may be utilized. The appropriate non-regular stimulation patterns or trains may depend or otherwise relate in some manner to the waveform shape selected. Specifically, the clinician may elect a particular waveform shape for the pulse generator16to apply. As noted above, the shape of such may be selected based upon the limitations of the pulse generator being utilized.

The method described above may be utilized to find patterns of non-constant inter-pulse interval deep brain stimulation trains that provide advantageous results, as defined by low frequency and low error rate. By way of a non-limiting example, genetic algorithms may be utilized to take the elected waveform shape and determine an improved pattern of non-constant inter-pulse intervals of deep brain stimulation trains. These non-constant inter-pulse intervals of deep brain stimulation trains may improve the efficiency and/or efficacy of the elected waveform shape, which may improve the efficiency and/or efficacy of the stimulation. This may allow a smaller or less robust pulse generator16to be utilized, provide a more efficacious result, provide a more efficient stimulation, or any combination of such. These factors may reduce the overall cost in implementing such electrical stimulation, may reduce the overall number of surgeries required over the life of the patient, and may provide a more beneficial result to the patient.

LITERATURE CITATIONS

Benabid A L, Pollak P, Gervason C, Hoffmann D, Gao D M, Hommel M, Perret J E, de Rougemont J (1991) Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus. Lancet. 337:403-6.Birdno M J “Analyzing the mechanisms of thalamic deep brain stimulation: computational and clinical studies”. Ph.D. Dissertation. Department of Biomedical Engineering, Duke University, Durham, N.C., USA, August 2009.Constantoyannis C, Kumar A, Stoessl A J, Honey C R (2004) Tremor induced by thalamic deep brain stimulation in patients with complex regional facial pain. Mov Disord. 19:933-936.Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, N.Y.Dorval A D, Kuncel A M, Birdno M J, Turner D A, Grill W M (2007) Deep brain stimulation alleviates Parkinsonian bradykinesia by regularizing thalamic throughput in human subjects. Society for Neuroscience Abstracts 32.Feng X J, Shea-Brown E, Greenwald B, Kosut R, Rabitz H (2007) Optimal deep brain stimulation of the subthalamic nucleus-a computational study. J Comput Neurosci. 23(3):265-282.Fogelson N, Kuhn A A, Silberstein P, Limousin P D, Hariz M, Trottenberg T, Kupsch A, Brown P (2005) Frequency dependent effects of subthalamic nucleus stimulation in Parkinson's disease. Neuroscience Letters 382:5-9.Grefenstette J J (1986) Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics 16:122-128.Grill W M, Cooper S E, Montgomery E B (2003) Effect of stimulus waveform on tremor suppression and paresthesias evoked by thalamic deep brain stimulation. Society for Neuroscience Abstracts 29.Kuncel A M, Cooper S E, Montgomery E B, Baker K B, Rezai A R, Grill W M (2006) Clinical response to varying the stimulus parameters in deep brain stimulation for essential tremor. Movement Disorders 21(11):1920-1928.Kupsch A, Klaffke S, Kuhn A A, Meissner W, Arnold G, Schneider G H, Maier-Hauff K, Trottenberg T (2003) The effects of frequency in pallidal deep brain stimulation for primary dystonia. J Neurol 250:1201-1204.Limousin P, Pollack P, Benazzouz A (1995) Effect on Parkinsonian signs and symptoms of bilateral stimulation. The Lancet 345:91-95.McIntyre C C, Grill W M, Sherman D L, Thakor N V (2004) Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition. J Neurophysiol 91:1457-1469.Rubin J E, Terman D (2004) High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model. J Comput Neurosci 16:211-235.Timmermann L, Gross J, Dirks M, Volkmann J, Freund H J, Schnitzler A (2003) The cerebral oscillatory network of parkinsonian resting tremor. Brain, 126:199-212.