Patent Application
20240261576
This application relates to Implantable Stimulator Devices (ISD), and more specifically to an algorithm for selecting electrodes and stimulation parameters in an ISD such as a Deep Brain Stimulation (DBS) device.
Implantable neurostimulator devices are devices that generate and deliver electrical stimuli to body nerves and tissues for the therapy of various biological disorders, such as pacemakers to treat cardiac arrhythmia, defibrillators to treat cardiac fibrillation, cochlear stimulators to treat deafness, retinal stimulators to treat blindness, muscle stimulators to produce coordinated limb movement, spinal cord stimulators to treat chronic pain, cortical and deep brain stimulators to treat motor and psychological disorders, and other neural stimulators to treat urinary incontinence, sleep apnea, shoulder subluxation, etc. The description that follows will generally focus on the use of the invention within a Deep Brain Stimulation (DBS) system, such as that disclosed in U.S. patent Application Publication 2020/0001091, which is incorporated herein by reference. However, the present invention may find applicability with any implantable neurostimulator device system, including Spinal Cord Stimulation (SCS) systems, Vagus Nerve Stimulation (VNS) system, Sacral Nerve Stimulation (SNS) systems, Peripheral Nerve Stimulation (PNS) systems, and the like.
A DBS system typically includes an Implantable Pulse Generator (IPG) 10 shown in
Lead wires 20 within the leads are coupled to the electrodes 16 and to proximal contacts 21 insertable into lead connectors 22 fixed in a header 23 on the IPG 10, which header can comprise an epoxy for example. Alternatively, the proximal contacts 21 may connect to lead extensions (not shown) which are in turn inserted into the lead connectors 22. Once inserted, the proximal contacts 21 connect to header contacts 24 within the lead connectors 22, which are in turn coupled by feedthrough pins 25 through a case feedthrough 26 to stimulation circuitry 28 within the case 12, which stimulation circuitry 28 is described below.
In the IPG 10 illustrated in
In a DBS application, as is useful in the treatment of tremor in Parkinson's disease for example, the IPG 10 is typically implanted under the patient's clavicle (collarbone). Leads 18 or 19 (perhaps as extended by lead extensions, not shown) are tunneled through and under the neck and the scalp, with the electrodes 16 implanted through holes drilled in the skull and positioned for example in the subthalamic nucleus (STN) and the pedunculopontine nucleus (PPN) in each brain hemisphere. The IPG 10 can also be implanted underneath the scalp closer to the location of the electrodes' implantation, as disclosed for example in U.S. Pat. No. 10,576,292. The IPG lead(s) 18 or 19 can be integrated with and permanently connected to the IPG 10 in other solutions.
IPG 10 can include an antenna 27a allowing it to communicate bi-directionally with a number of external devices and systems discussed subsequently. Antenna 27a as shown comprises a conductive coil within the case 12, although the coil antenna 27a can also appear in the header 23. When antenna 27a is configured as a coil, communication with external systems preferably occurs using near-field magnetic induction. IPG 10 may also include a Radio-Frequency (RF) antenna 27b. In
Stimulation in IPG 10 is typically provided by pulses each of which may include a number of phases such as 30a and 30b, as shown in the example of
These and possibly other stimulation parameters taken together comprise a stimulation program that the stimulation circuitry 28 in the IPG 10 can execute to provide therapeutic stimulation to a patient.
In the example of
IPG 10 as mentioned includes stimulation circuitry 28 to form prescribed stimulation at a patient's tissue.
Proper control of the PDACs 40iand NDACs 42i allows any of the electrodes 16 and the case electrode Ec 12 to act as anodes or cathodes to create a current (such as the pulses described earlier) through a patient's tissue, Z, hopefully with good therapeutic effect. In the example shown, and consistent with the first pulse phase 30a of
Other stimulation circuitries 28 can also be used in the IPG 10. In an example not shown, a switching matrix can intervene between the one or more PDACs 40i and the electrode nodes ei 39, and between the one or more NDACs 42i and the electrode nodes. Switching matrices allow one or more of the PDACs or one or more of the NDACs to be connected to one or more electrode nodes at a given time. Various examples of stimulation circuitries can be found in U.S. Pat. Nos. 6,181,969, 8,606,362, 8,620,436, U.S. patent Application Publications 2018/0071520 and 2019/0083796.
Much of the stimulation circuitry 28 of
Also shown in
Referring again to
External controller 60 can be as described in U.S. patent Application Publication 2015/0080982 for example, and may comprise a portable, hand-held controller dedicated to work with the IPG 10. External controller 60 may also comprise a general-purpose mobile electronics device such as a mobile phone which has been programmed with a Medical Device Application
(MDA) allowing it to work as a wireless controller for the IPG 10, as described in U.S. patent Application Publication 2015/0231402. External controller 60 includes a display 61 and a means for entering commands, such as buttons 62 or selectable graphical icons provided on the display 61. The external controller 60's user interface enables a patient to adjust stimulation parameters, although it may have limited functionality when compared to systems 70 and 80, described shortly. The external controller 60 can have one or more antennas capable of communicating with a compatible antenna in the IPG 10, such as a near-field magnetic-induction coil antenna 64a and/or a far-field RF antenna 64b.
Clinician programmer 70 is described further in U.S. patent Application Publication 2015/0360038, and can comprise a computing device such as a desktop, laptop, or notebook computer, a tablet, a mobile smart phone, a Personal Data Assistant (PDA)-type mobile computing device, etc. In
External system 80 comprises another means of communicating with and controlling the IPG 10 via a network 85 which can include the Internet. The network 85 can include a server 86 programmed with IPG communication and control functionality, and may include other communication networks or links such as WiFi, cellular or land-line phone links, etc. The network 85 ultimately connects to an intermediary device 82 having antennas suitable for communication with the IPG's antenna, such as a near-field magnetic-induction coil antenna 84a and/or a far-field RF antenna 84b. Intermediary device 82 may be located generally proximate to the IPG 10. Network 85 can be accessed by any user terminal 87, which typically comprises a computer device associated with a display 88. External system 80 allows a remote user at terminal 87 to communicate with and control the IPG 10 via the intermediary device 82.
A method is disclosed for optimizing stimulation for a patient having a stimulator device, wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation, the method providing test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter. The method may comprise: (a) receiving information about tissue structures surrounding the electrode array; (b) processing the information to determine a first data set indicative of the desirability of providing stimulation at possible of the combinations; (c) executing an algorithm to provide test stimulation at a plurality of the combinations, wherein the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination and the first data set; and (d) using the scores at the tested combinations to determine an optimal therapeutic stimulation for the patient.
In one example, each at least one score is indicative of the effectiveness of the test stimulation at the tested combinations. In one example, the electrode array is implanted in a brain of the patient. In one example, at least one of the plurality of electrodes comprises a ring electrode which is circumferential around a longitudinal position in the electrode array. In one example, at least two of the plurality of electrodes comprise split-ring electrodes at a common longitudinal position in the electrode array. In one example, the at least one stimulation parameter comprises an amplitude. In one example, the positions vary longitudinally in the electrode array. In one example, the positions vary rotationally in the electrode array. In one example, the at least one score is indicative of a patient symptom, a patient response, or a side effect in response to the test stimulation. In one example, the optimal therapeutic stimulation comprises an optimal combination of a position and a value of the at least one stimulation parameter. In one example, the optimal therapeutic stimulation indicates which of the electrodes should be active to provide the optimal therapeutic stimulation, the polarity of the active electrodes, and an amplitude at the active electrodes. In one example, the first data set comprises a value indicative of the desirability of providing stimulation at the possible combinations. In one example, the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination, the first data set, and at least one other data set. In one example, the at least one other data set is determined at the possible combinations. In one example, the at least one other data set at the possible combinations is determined using a distance between the possible combinations and each of the previously tested combinations. In one example, the at least one other data set at the possible combinations is determined using the at least one score for each previously tested combination. In one example, the first data set and the at least one other data set are weighted to determine a weighted data set at the possible combinations. In one example, the next combination is determined as the possible combination having a best value in the weighted data set. In one example, a plurality of scores are determined for each previously tested combination. In one example, the algorithm is further configured to iteratively redetermine the first data set using the at least one score for each previously tested combination. In one example, the information about tissue structures surrounding the electrode array comprises tissue imaging information.
A system is disclosed, which may comprise: a stimulator device comprising a plurality of electrodes in an electrode array for providing stimulation; and an external device for optimizing stimulation for a patient having the stimulator device, the external device configured to communicate with the stimulator device to provide test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter, wherein the external device is configured to: (a) receive information about tissue structures surrounding the electrode array; (b) process the information to determine a first data set indicative of the desirability of providing stimulation at possible of the combinations; (c) execute an algorithm to provide test stimulation at a plurality of the combinations, wherein the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination and the first data set; and (d) use the scores at the tested combinations to determine an optimal therapeutic stimulation for the patient.
In one example, each at least one score is indicative of the effectiveness of the test stimulation at the tested combinations. In one example, the electrode array is configured to be implanted in a brain of the patient. In one example, at least one of the plurality of electrodes comprises a ring electrode which is circumferential around a longitudinal position in the electrode array. In one example, at least two of the plurality of electrodes comprise split-ring electrodes at a common longitudinal position in the electrode array. In one example, the at least one stimulation parameter comprises an amplitude. In one example, the positions vary longitudinally in the electrode array. In one example, the positions vary rotationally in the electrode array. In one example, the at least one score is indicative of a patient symptom, a patient response, or a side effect in response to the test stimulation. In one example, the optimal therapeutic stimulation comprises an optimal combination of a position and a value of the at least one stimulation parameter. In one example, the optimal therapeutic stimulation indicates which of the electrodes should be active to provide the optimal therapeutic stimulation, the polarity of the active electrodes, and an amplitude at the active electrodes. In one example, the first data set comprises a value indicative of the desirability of providing stimulation at the possible combinations. In one example, the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination, the first data set, and at least one other data set. In one example, the at least one other data set is determined at the possible combinations. In one example, the at least one other data set at the possible combinations is determined using a distance between the possible combinations and each of the previously tested combinations. In one example, the at least one other data set at the possible combinations is determined using the at least one score for each previously tested combination. In one example, the first data set and the at least one other data set are weighted to determine a weighted data set at the possible combinations. In one example, the next combination is determined as the possible combination having a best value in the weighted data set. In one example, a plurality of scores are determined for each previously tested combination. In one example, the algorithm is further configured to iteratively redetermine the first data set using the at least one score for each previously tested combination. In one example, the information about tissue structures surrounding the electrode array comprises tissue imaging information.
A non-transitory computer readable medium is disclosed comprising instructions operable in conjunction with a stimulator device, wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation, wherein the instructions when executed are configured provide test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter, wherein the instructions are further configured to: (a) receive information about tissue structures surrounding the electrode array; (b) process the information to determine a first data set indicative of the desirability of providing stimulation at possible of the combinations; (c) execute an algorithm to provide test stimulation at a plurality of the combinations, wherein the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination and the first data set; and (d) use the scores at the tested combinations to determine an optimal therapeutic stimulation for the patient.
A method is disclosed for optimizing stimulation for a patient having a stimulator device, wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation, the method providing test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter. The method may comprise: (a) receiving information about tissue structures surrounding the electrode array; (b) processing the information to determine a first data set indicative of the desirability of providing stimulation at each possible of the combinations; (c) providing test stimulation at initial of the combinations; (d) determining at least one score for each of the initial combinations, wherein each at least one score is indicative of the effectiveness of the test stimulation at the tested combinations; (e) determining a next combination using at least all previously determined scores and the first data set; (f) repeating steps (c)-(e) for the next combination to determine and test further next combinations until a stopping criterium is met; and (g) using the scores to determine an optimal therapeutic stimulation for the patient.
In one example, the electrode array is implanted in a brain of the patient. In one example, at least one of the plurality of electrodes comprises a ring electrode which is circumferential around a longitudinal position in the electrode array. In one example, at least two of the plurality of electrodes comprise split-ring electrodes at a common longitudinal position in the electrode array. In one example, the at least one stimulation parameter comprises an amplitude. In one example, the positions vary longitudinally in the electrode array. In one example, the positions vary rotationally in the electrode array. In one example, the at least one score is indicative of a patient symptom, a patient response, or a side effect in response to the test stimulation. In one example, the optimal therapeutic stimulation comprises an optimal combination of a position and a value of the at least one stimulation parameter. In one example, the optimal therapeutic stimulation indicates which of the electrodes should be active to provide the optimal therapeutic stimulation, the polarity of the active electrodes, and an amplitude at the active electrodes. In one example, the first data set comprises a value indicative of the desirability of providing stimulation at each of the possible combinations. In one example, step (e) further comprises determining at least one other data set at each possible combination, and wherein step (e) comprises determining a next combination using at least all previously determined scores, the first data set, and the at least one other data set. In one example, the at least one other data set at each possible combination is determined using all previously determined scores. In one example, the at least one other data set at each possible combination is determined using a distance between each possible combination and each of the previously tested combinations. In one example, the first data set and the at least one other data set are weighted to determine a weighted data set at each possible combination. In one example, the next combination is determined in step (e) using the weighted data set. In one example, the next combination is determined in step (e) as the possible combination having a best value in the weighted data set. In one example, a plurality of scores are determined for each initial combination in step (d). In one example, step (f) further comprises, as an initial sub-step and prior to repeating the steps prescribed in steps (c)-(e), redetermining the first data set using the at least one scores. In one example, the information about tissue structures surrounding the electrode array comprises tissue imaging information.
A system is disclosed, which may comprising: a stimulator device comprising a plurality of electrodes in an electrode array for providing stimulation; and an external device for optimizing stimulation for a patient having the stimulator device, the external device configured to communicate with the stimulator device to provide test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter, wherein the external device is configured to: (a) receive information about tissue structures surrounding the electrode array; (b) process the information to determine a first data set indicative of the desirability of providing stimulation at each possible of the combinations; (c) cause the stimulator device to provide test stimulation at initial of the combinations; (d) receive at least one score for each of the initial combinations, wherein each at least one score is indicative of the effectiveness of the test stimulation at the tested combinations; (e) determine a next combination using at least all previously determined scores and the first data set; (f) repeat steps (c)-(e) for the next combination to determine and test further next combinations until a stopping criterium is met; and (g) use the scores to determine an optimal therapeutic stimulation for the patient.
In one example, the electrode array is configured to be implanted in a brain of the patient. In one example, at least one of the plurality of electrodes comprises a ring electrode which is circumferential around a longitudinal position in the electrode array. In one example, at least two of the plurality of electrodes comprise split-ring electrodes at a common longitudinal position in the electrode array. In one example, the at least one stimulation parameter comprises an amplitude. In one example, the positions vary longitudinally in the electrode array. In one example, the positions vary rotationally in the electrode array. In one example, the at least one score is indicative of a patient symptom, a patient response, or a side effect in response to the test stimulation. In one example, the optimal therapeutic stimulation comprises an optimal combination of a position and a value of the at least one stimulation parameter. In one example, the optimal therapeutic stimulation indicates which of the electrodes should be active to provide the optimal therapeutic stimulation, the polarity of the active electrodes, and an amplitude at the active electrodes. In one example, the first data set comprises a value indicative of the desirability of providing stimulation at each of the possible combinations. In one example, (e) comprises determine at least one other data set at each possible combination, and wherein (e) comprises determining a next combination using at least all previously determined scores, the first data set, and the at least one other data set. In one example, the at least one other data set at each possible combination is determined using all previously determined scores. In one example, the at least one other data set at each possible combination is determined using a distance between each possible combination and each of the previously tested combinations. In one example, the first data set and the at least one other data set are weighted to determine a weighted data set at each possible combination. In one example, the next combination is determined in (e) using the weighted data set. In one example, the next combination is determined in (e) as the possible combination having a best value in the weighted data set. In one example, (d) comprises receive a plurality of scores for each of the initial combinations. In one example, (f) comprises redetermine first the data set using the at least one scores prior to repeating the steps prescribed in steps (c)-(e). In one example, the information about tissue structures surrounding the plurality of electrodes comprises tissue imaging information.
A non-transitory computer readable medium is disclosed comprising instructions operable in conjunction with a stimulator device, wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation, wherein the instructions when executed are configured provide test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter, wherein the instructions are further configured to: (a) receive information about tissue structures surrounding the electrode array; (b) process the information to determine a first data set indicative of the desirability of providing stimulation at each possible of the combinations; (c) cause the stimulator device to provide test stimulation at initial of the combinations; (d) receive at least one score for each of the initial combinations, wherein each at least one score is indicative of the effectiveness of the test stimulation at the tested combinations; (e) determine a next combination using at least all previously determined scores and the first data set; (f) repeat steps (c)-(e) for the next combination to determine and test further next combinations until a stopping criterium is met; and (g) use the scores to determine an optimal therapeutic stimulation for the patient.
The GUI 99 may include a waveform interface 104 where various aspects of the stimulation can be selected or adjusted. For example, waveform interface 104 allows a user to select an amplitude (e.g., a current I), a frequency (F), and a pulse width (PW) of the stimulation pulses. Waveform interface 104 can be significantly more complicated, particularly if the IPG 10 supports the provision of stimulation that is more complicated than a repeating sequence of pulses. Waveform interface 104 may also include inputs to allow a user to select whether stimulation will be provided using biphasic (
The GUI 99 may also include an electrode configuration interface 105 which allows the user to select a particular electrode configuration specifying which electrodes should be active to provide the stimulation, and with which polarities and relative magnitudes. In this example, the electrode configuration interface 105 allows the user to select whether an electrode should comprise an anode (A) or cathode (C) or be off, and allows the amount of the total anodic or cathodic current +I or −I (specified in the waveform interface 104) that each selected electrode will receive to be specified in terms of a percentage, X. For example, in
Once the waveform parameters (104) and electrode configuration parameters (105) are determined, they can be sent from the clinician programmer 70 to the IPG 10, so that the IPG's stimulation circuitry 28 (
Use of selected electrodes to provide cathodic stimulation sets a particular position for a cathodic pole 120 in three-dimensional space. The position of this cathode pole 120 can be quantified at a particular longitudinal position L along the lead (e.g., relative to a point on the lead such as the longitudinal position of electrode E15), and at a particular rotational angle θ (e.g., relative to a the center of electrode E15). Rotation angle θ of the stimulation is typically only relevant when a directional lead such as 19 (
Notice that the position of the pole 120 (L,θ) may be virtual; that is, the position may not necessarily occur at the physical position of any of the electrodes 16 in the electrode array, as explained further later. The leads interface 102 preferably also includes an image 103 of the lead being used (e.g., directional lead 19) for the patient. Although not shown, the leads interface 102 can include a selection to access a library of relevant images 103 of the types of leads (e.g., 18 or 19) that may be implanted in different patients, which may be stored with the relevant external system software (e.g., 96,
An electrode configuration algorithm (not shown), operating as part of external system's software 96, can determine the position (L,θ) of the cathode pole 120 in three-dimensional space from a given electrode configuration (105), and can also conversely determine an electrode configuration from a given position of the pole 120. For example, the user can place the position of the pole 120 in leads interface 102 using the cursor 101. The electrode configuration algorithm can then be used to compute an electrode configuration that best places the pole 120 in this position. Note in the example shown that the cathode pole 120 is positioned closest to electrodes E8, E9, E11, and E12, and at a longitudinal position between them (i.e., that is between the longitudinal positions of E8 and E9, and E11 and E12). The electrode configuration algorithm may thus calculate based on this position that these electrodes should receive the largest share of cathodic current (20%*−I), as shown in the electrode configuration interface 105. E10 and E13 which are farther away from the pole 120 may receive lesser percentages *10%*−I), again as shown at 105. The electrode configuration algorithm can also operate in reverse: from a given electrode configuration, the position of the pole 120 can be determined. An electrode configuration algorithm is described further in U.S. patent Application Publication 2019/0175915, which is incorporated herein by reference.
GUI 99 can further include a visualization interface 106 that allows a user to view a stimulation field image 112 formed on a lead given the selected stimulation parameters and electrode configuration. The stimulation field image 112 is formed by field modelling in the clinician programmer 70, as discussed further in the '091 Publication.
The visualization interface 106 preferably, but not necessarily, further includes tissue imaging information 114. This tissue imaging information 114 is presented in
The tissue imaging information 114 is preferably registered to the lead 19 such that the position of the lead 19 (and the electrodes) within the tissue imaging information 114 (and hence the patient's tissue) is known. This allows the GUI 99 to overlay the lead image 111 and the stimulation field image 112 with the tissue imaging information 114 in the visualization interface 106 so that the position of the stimulation field 112 relative to the various tissue structures 114i can be visualized. The various images shown in the visualization interface 106 (i.e., the lead image 111, the stimulation field image 112, and the tissue structures 114i) can be three-dimensional in nature, and hence may be rendered to allow such three-dimensionality to be better appreciated by the user, such as by shading or coloring the images, etc. A view adjustment interface 107 may allow the user to move or rotate the images, using cursor 101 for example, as explained in the '091 Publication. In
The GUI 99 of
Whether tissue structures should be stimulated or avoided can depend on a number of factors, such as the patient's diagnosis or the symptoms he is experiencing. For example, a patient with predominant tremor might benefit from stimulating the dorsal part of the subthalamic nucleus (STN) closer to the Internal Capsule, whereas a patient with predominant gait problems might benefit from stimulating the ventral part of the STN closer to the Substantia Nigra, and thus these tissue structures may correspond to tissue structures 114a. One skilled in the art would similarly understand tissues structures that should be avoided when providing DBS stimulation therapy corresponding to tissue structures 114c.
Especially in a DBS application, it is important that correct stimulation parameters be determined for a given patient. Improper stimulation parameters may not yield effective relief of a patient's symptoms, or may cause unwanted side effects. To determine proper stimulation, a clinician typically uses a GUI such as GUI 99 to try different combinations of stimulation parameters. This may occur, at least in part, during a DBS patient's surgery when the leads are being implanted. Such intra-operative determination of stimulation parameters can be useful to determine a general efficacy of DBS therapy. However, finalizing stimulation parameters that are appropriate for a given DBS patient typically occurs after surgery after the patient has had a chance to heal, and after the position of the leads stabilize in the patient. Thus, at such time, the patient will typically present to the clinician's office to determine (or further refine) optimal stimulation parameters during a programming session.
Gauging the effectiveness of a given set of stimulation parameters typically involves programming the IPG 10 with that set, and then reviewing the therapeutic effectiveness and side effects that result. Therapeutic effectiveness and side effects are often assessed by one or more different scores (S) for one or more different clinical responses, which are entered into the GUI 99 of the clinician programmer 70 where they are stored with the stimulation parameters set being assessed. Such scores can be subjective in nature, based on patient or clinician observations. For example, bradykinesia (slowness of movement), rigidity, tremor, or other symptoms or side effects, can be scored by the patient, or by the clinician upon observing or questioning the patient. Such scores in one example can range from 0 (best) to 4 (worst).
Scores can also be objective in nature based on measurements taken regarding a patient's symptoms or side effects. For example, a Parkinson's patient may be fitted with a wearable sensor that measures tremors, such as by measuring the frequency and amplitude of such tremors. A wearable sensor may communicate such metrics back to the GUI 99, and if necessary, converted to a score. U.S. Patent Application Publication 2021/0196956, which is incorporated herein by reference in its entirety, discusses determining which symptoms and/or side effects are most sensible to score for a given patient.
One type of objective measurement proposed for use in DBS systems are Evoked Resonant Neural Activity (ERNA) responses, which are described in U.S. patent Application p Publication 2023/0099390; Sinclair, et al., “Subthalamic Nucleus Deep Brain Stimulation Evokes Resonant Neural Activity,” Ann. Neurol. 83(5), 1027-31 (2018). An ERNA comprises an oscillatory voltage response provided by brain tissue in response to stimulation. Stimulation of the STN, and particularly of the dorsal subregion of the STN, has been observed to evoke strong ERNA responses, whereas stimulation of the posterior subthalamic area (PSA) does not evoke such responses. Thus, ERNA may provide a biomarker for selecting appropriate electrodes for stimulation and for achieving the desired therapeutic response. Having said this, ERNA is just one type of neural potential that can be monitored in response to stimulation. Local field potentials (LFPs), DBS local evoked potentials (DLEPs), evoked compound activity (ECA), single or multi unit activities (SUA/MUA), or even EEG/ECoG may also be monitored and used in the disclosed techniques. Other brain regions which may evoke or not evoke ERNA may also be employed. The '390 Publication discloses an IPG capable of sensing neural potentials such as ERNAs, and discusses algorithms for interpreting and using such sensed data. Further, U.S. patent Application Publication 2023/0271015 discusses the use of ENRA measurement as scores used in algorithms to assist in the selection of optimal stimulation parameters for a DBS patient.
Once the GUI 99 of the clinician programmer 70 has received various scores S (whether subjective or objective, and whether from patient or clinician) for each of the sets of stimulation parameters tested, the clinician may review the scores to try and determine one or more sets of optimal stimulation parameters for the patient which maximize therapeutic effectiveness while minimizing unwanted side effects. Typically, this process involves significant guess work and time, especially when a directional lead such as 19 (
In reality, it may only be necessary to optimize the waveform parameter of amplitude (I), as other stimulation parameters (frequency F, pulse width PW) may be known or determined by other means. Nevertheless, at least one score would need to be determined for all possible combinations of I, L and θ the IPG 10 is capable of producing. This may be a burdensome number of combinations to try during a programming session. At any given combination of I, L and θ, it may take some time (e.g., a matter of minutes) to determine an appropriate score (S). This is particularly true if the scores are based on subjective measurements, because the patient may need to be observed, may need to perform certain tasks (finger tapping, walking, etc.), or may need to answer a number of questions. Because a programming session may only reasonably last a few hours, only a fraction of possible I, L and θ combinations can be tested and scored. While scores provided by objective measurements such as those based on ERNA neural response measurements may be quicker to perform, it may still be inefficient to measure such responses at each possible combination of I, L and θ. This makes it difficult to determine optimal stimulation parameters for the patient.
U.S. patent Application Publication 2022/0257950, which is incorporated by reference in its entirety, discloses an optimization algorithm 200 to efficiently test different I, L and θ combinations with the goal of more quickly arriving at optimal stimulation parameters for a given patient. As stated in the '950 Publication, another waveform parameter (e.g., F, PW) could also be optimized, but the waveform parameter of amplitude (I) is chosen for optimization given its high significance to patient treatment.
This disclosure improves upon the optimization algorithm provided in the '950 Publication. In particular, the optimization algorithm here includes anatomical information as an input to the algorithm. The anatomical information may comprise the tissue imaging information 114 described earlier with respect to
Optimization algorithm 200 is shown at a high level in
Once Lopt is determined, the algorithm 200 determines whether Lopt is proximate to split ring electrodes (370)—i.e., whether Lopt is longitudinally at or close to split ring electrodes on a directional lead 19. Lopt may be determined to be proximate to split ring electrodes if at least one split ring electrode is used (active) to set the position of Lopt, as explained further below. If Lopt is not proximate to split ring electrodes, as would necessarily be the case when the algorithm 200 is used with a non-directional lead (e.g., 18,
If Lopt is proximate to split ring electrodes (370), as might be the case when algorithm 200 is used with a directional lead 19, the algorithm 200 simultaneously determines an optimal rotational angle (θopt) and amplitude (Iopt2) for stimulation around the lead at the optimized longitudinal position Lopt (400). Algorithm 200 may determine that Iopt2 is the same as Iopt1 determined earlier, but it is also likely that Iopt2 will differ from Iopt1 as the rotational angle of stimulation is also optimized. As explained further below, determining θopt and Iopt2 involves the algorithm 200 selecting various values for rotational angle θ and amplitude I (θ,I) which can be tried on the patient and scored. This process can be similar to the manner in which (L,I) values were selected earlier, with the algorithm 200 automatically and efficiently determining next (θ,I) values to be tested and scored based on previously tested and scored (θ,I) values. Once θopt and Iopt2 are optimized, the stimulation is fully optimized for the patient, as the longitudinal position, rotational angle, and amplitude of the stimulation (Lopt, θopt, Iopt2) have now been determined (450).
One skilled in the art will appreciate that programming algorithm 200 can comprise a portion of software 96 operable in the clinician programmer 70 or other external system (
L,I parameter space 210 shows possible values (L,I) that can be tested and optimized, which is particularly useful during step 300 (
θ,I Parameter space 220 shows possible values for (θ,I) that can be tested and optimized, which is particularly useful during step 400 (
In a first optional step 305, certain (L,I) values in the L,I parameter space 210 may at the outset be excluded from further consideration and testing in the algorithm 200. Such exclusions are useful to reduce the number of (L,I) values that must be assessed and potentially tested by the algorithm 200, reducing computational complexity. Exclusion of (L,I) values may be manual (by using input from the clinician into the GUI 99), or automated, or semi-automated, in the algorithm 200. Furthermore, some amount of cursory patient testing—providing stimulation at various longitudinal positions L and/or amplitudes I—can be useful at step 305 in generally determining and inputting into the algorithm 200 (L,I) values that should be excluded.
Exclusions at step 305 may occur for a number of different reasons. First, stimulation provided at certain (L,I) values may simply be understood (e.g., based on testing) as unlikely to provide therapeutic effectiveness. Thus, stimulation at particular longitudinal positions L along the lead 19 may simply be too far away from tissue structures of interest. Certain amplitude values I the IPG can produce may simply be too low or too high to be expected to be useful. (L,I) values corresponding to these longitudinal positions and amplitudes may therefore simply be excluded.
Exclusion of certain (L,I) values may also occur step 305 based on objective testing. In a preferred example, such objective testing can comprise measuring neural potentials (e.g., ERNAs) discussed earlier. This was discussed in detail in the above-incorporated '015 Publication.
Exclusion of certain (L,I) values may also occur step 305 based on an analysis of the surrounding tissue structures, which may involve the use of tissue imaging information 114, as discussed earlier with reference to
The lead 19 may have electrodes that are proximate to different tissue structures that are more (114a) or less (114c) desirable to stimulate. Electrodes at longitudinal positions L proximate to less such desirable tissue structures (114c) may thus be excluded, or higher amplitudes I may be excluded at such electrodes to reduce the risk of recruitment of such structures. Exclusions of various (L,I) values based on tissue imaging information 114 may be manual based on clinician assessment and input, or automated using a tissue analysis algorithm 276. This tissue analysis algorithm 276 is discussed later (see
Referring again to
A data set 230 is formed in the clinician programmer 70 as the algorithm 200 runs, and includes the electrode configurations necessary to form stimulation at the prescribed longitudinal positions, L. For example, and referring to illustration of lead 19 in
Longitudinal position L=6 (step i=2 for the second present value) corresponds to the location of ring electrode E2, which will receive 100% of the cathodic current (100%*−I) to place cathode pole 120 longitudinally at this position. More specifically, because I=2mA at this step i=2, electrode E2 will receive −2.0 mA at this step.
Longitudinal position L=3.5 (step i=3 for the third preset value) is directly between ring electrode E4, and the longitudinal position of split ring electrodes E5, E6, and E7. Therefore, to place the cathode pole 120 (virtually) as this position, the cathodic current is shared equally between E4 (50%*−I) and E5, E6, and E7 as a group (with each receiving 16.7%*−I). More specifically, because I=3.5mA at this step i=3, each of electrode E5, E6, and E7 will receive −0.6 mA at this step (rounded), with E4 receiving −1.7 mA. Although not shown, remember that these electrode configurations as reflected in data set 230 are determinable in the external system software 96 using the electrode configuration algorithm described earlier, which can comprise a portion of optimization algorithm 200.
The stimulation parameters as embodied in the (L,I) presets and as determined by the electrode configuration algorithm (the active electrodes; whether they are anodes or cathodes, and the amplitude at each active electrode) are sequentially transmitted to the patient's IPG 10 (along with other non-optimized parameters such as frequency F and pulse width PW) so that the stimulation can be applied to the patient. As each of these stimulation parameters sets are applied, at least one score (S) is then determined for each (315). As noted above, a score can comprise any metric (subjective or objective) that indicates therapeutic effectiveness of and/or the side effects resulting from the stimulation parameters sets. As assumed earlier, a lower score in the depicted example indicates a better result, with 0 being good and 4 poor, although a different scale could be used in which higher numbers are better. The scores (S) once determined for each of the presets are entered into the data set 230 in the clinician programmer 70, such as by having the clinician type the score into the GUI 99 (see
After sequentially applying stimulation according to these presets and determining and recording their scores S after patient testing, the optimization algorithm 200 can determine a best of the (L,I) values (Lopt, Iopt1) based on the scores at those points (320). As explained further below, as the algorithm 200 iterates, more (L,I) values will be tested and scored, and (Lopt, Iopt1) can be updated accordingly at this step. At this point, after only testing the presets, (Lopt, Iopt1) is determined at step 320 to be (3.5,3.5), because this tested value yields the best (e.g., lowest) score (of 0.5).
Next, the algorithm 200 determines whether one or more stopping criteria have been met (325). If a stopping criterium has been met (325), the algorithm 200 may stop iterating—i.e., stop determining and testing further (L,I) values—at which point (Lopt, Iopt1) can be established. Any number of stopping criteria can be used. For example, the algorithm 200 may decide to stop: if a last determined (L,I) value is too close to other values that have been tested; if the scores at a number of preceding (L,I) values are poor (suggesting that the algorithm is no longer suggesting new (L,I) values to useful effect); if a score at the last selected value is significantly good (suggesting that the algorithm can simply select this last value as the optimal point); if a maximum number of steps (i) has been reached; etc. The stopping criteria need not be automated in the algorithm 200. For example, a stopping criterium may simply comprise the clinician deciding that a suitable number of (L,I) values has been tested and no further steps are required.
If a stopping criteria has not been met (325), the algorithm 200 proceeds to determine a next (L,I) value to be tested (330) in a next iteration (step i=4). Details involved in choosing this next (L,I) value are shown first with respect to
Step 330a calculates factor RA for all (L,I) positions using an inverse distance metric, as shown in
Note as shown in the equation in
Returning to
Returning to
Returning to
This data set 270 may be preset and not based on the position of or scores at previously tested values. For example, RDmay be higher at (L,I) points having higher amplitudes (e.g., RD=4 when I>5 mA) and lower at (L,I) values having lower amplitudes (e.g., RD=0 when I<1 mA). These preset values are shown in data set 270 using shading, with more-preferred lower amplitude having a lighter shading, and less-preferred higher amplitudes having a higher shading.
Returning to
The values RE are preferably determined from the tissue imaging information 114 described earlier with reference to
Analysis of the tissue imaging information 114 and the resulting populated values RE in data set 275 can be manual or automated. In a manual method, the clinician can look at the position of the lead 19 in the tissue imaging information 114 from the GUI 99, and input values RE into the
GUI 99. For example, the clinician understanding that tissue structure 114a is desirable to stimulate and recruit, may determine that better (lower) values RE should be associated with longitudinal positions having electrodes proximate to this structure 114a. Thus, roughly speaking, the data set 275 has been populated with better (lighter shaded) values at higher longitudinal positions (e.g., RE=0 when 2<L<6). By contrast, the clinician understanding that tissue structure 114c is not desirable to stimulate and recruit, perhaps because the stimulation of tissue structure 114c causes side effects, may determine that worse (higher) values RE should be associated with longitudinal positions having electrodes proximate to this structure 114c. Thus, roughly speaking, the data set 275 has been populated with worse (darker shaded) values at lower longitudinal positions (e.g., RE=3 when 0<L<1). Other values in the data set 275 may include intermediate REvalues (e.g., RE=1.5 when 1<L<2 and 6<L<7), either because these longitudinal positions are between tissue structures 114a and 114c, or proximate to tissue structures 114b for which stimulation appears neither effective or problematic.
As described earlier, whether tissue structures should be stimulated or avoided can depend on a number of factors, and these can be reflected in the RE values populated in data set 275. For example, a patient with predominant tremor might benefit from stimulating the dorsal part of the subthalamic nucleus (STN) closer to the Internal Capsule, whereas a patient with predominant gait problems might benefit from stimulating the ventral part of the STN closer to the Substantia Nigra. Longitudinal positions proximate to these preferred tissue structures (e.g., 114a) could be given better (lower) RE values in the data set 275. One skilled in the art would similarly understand that longitudinal positions proximate to tissues structures that should be avoided (e.g., 114c) could be given worse (higher) RE values.
Notice in
While the RE values in data set 275 may be constant for a given L value, that is not necessarily the case, as shown in
As noted earlier, an analysis of the tissue imaging information 114 and the resulting populated values RE in data set 275 can be automated, such as through the use of a tissue analysis algorithm 276. As one skilled in the art will appreciate, such an algorithm 276 would essentially act as explained above to assess the tissue imaging information 114 at the different longitudinal positions, to identify the particular tissues structures and the extent to which they are preferably stimulated or not. Thus, algorithm 276 can automatically populate the RE values in data set 275 to encourage the selection of (lower) (L,I) values that are closer to tissue structures that are preferable to stimulate (e.g., 114a, such as the dorsal part of the STN closer to the Internal Capsule or the ventral part of the STN, as mentioned earlier) and to discourage the selection of (higher) (L,I) values that are closer to tissue structures that are preferable to avoid (e.g., 114c). Note that some amount of manual input may be involved even when tissue analysis algorithm 276. For example, the clinician may use the GUI to indicate particular tissue structures 114i in the tissue imaging information 114, to indicate whether stimulation or avoidance of such tissue structures is advisable, etc. As discussed earlier with reference to
Algorithm 200 doesn't require the use of all of the factors described in
Returning to
The weights w applied to the factors can be varied based on user preferences, and
Weight wE associated with data set 275 (
Note that weighting of the factors to arrive at RW(L,I) can involve some amount of processing of the individual factors RI(L,I). In this regard, note that each of the individual factors RI(L,I) may be of different magnitudes, depending on how such factors are computed. As such, it may be beneficial to normalize the different factors RI(L,I) so that their magnitudes are generally equated before these factors are weighted by weights wI. Alternatively, the weights wI themselves may be adjusted to accomplish such normalization, so that the individual contributions provided by wI*RI(L,I) leading to RW(L,I) are generally equal in magnitude. In another alternative, each of the (L,I) values for a given factor RI can be ranked, with for example a best (lowest) value (L,I) being given a best (e.g., lowest) ranking (e.g., 1), and a worst (highest) value (L,I) being given a worst (highest) ranking (e.g., L*I). Ranking each (L,I) value for each factor RI before weighting tends to normalize the values of each of the factors, making their weighting by wI more meaningful.
Returning to
The upper left example of the RW(L,I) data set 280 shows different examples of exclusion zones 335a, which comprises zones of (L,I) values that are logical to exclude from testing for one reason or another. Such exclusions zones 335a may be based on the same factors considered earlier at step 305 (
The upper right of
Exclusion zones may 335c be also placed around already-tested (L,I) values, as shown at the bottom left of
As explained further below, the algorithm 200 will eventually iterate to select a new (L,I) value to test and score, as shown at the bottom right of
To summarize, the algorithm 200 can apply various exclusion rules 330h to exclude one or more less-meaningful values to prevent such values from being next selected for testing, at least during a next iteration.
Although not shown in
Referring to
Note that prior data determined upon testing of the patient can be used in place of, or can comprise, a preset value. Further, presets do not necessarily need to be pre-established at set (L,I) points. Instead, the clinician can simply start testing at a particular (L,I) value, record a score, etc. Eventually, when the algorithm 200 has received enough scores at previously-tested (L,I) values, it can begin to automatically determine next values at step 330, and the algorithm can begin to iterate.
Referring again to
At this point, the algorithm 200 can return to step 320, where a best of the tested values (Lopt, Iopt1) is determined and/or updated. As described earlier, this involves looking at the scores associated with each of the previously-tested points (in steps i=1 to 4 to this point). The best (e.g., lowest) of these scores (0.5) is still associated with step i=3 at this point, and so (Lopt, Iopt1) remains (3.5, 3.5), which is not updated.
As the algorithm 200 continues, it again determines if one or more stopping criteria have been met (325). Assuming this doesn't occur, the algorithm 200 determines a next (L,I) value to be tested (330) in a next iteration of the algorithm (step i=5). Determining this next (L,I) value essentially occurs as described earlier by determining factors RA-RE at all points (L,I) (excepting excluded points). However, notice that there are now more previously-tested points (L,I) to consider (i.e., four, instead of the initial three presets), meaning that the sums in the equations shown in
The algorithm 200 as illustrated assumes that the tissue structure factor RE(
This next (L,I) value is applied (340) and scored (345) (S=2); (Lopt, Iopt1) is determined and possibly updated (320), etc. The effect of such successive iterations of the algorithm 200 is shown in the data set 230 and the L,I parameter space 210 of
Once a stopping criterium has been met (325), an optimal value of (L,I)—(Lopt, Iopt1)—is determined, which would comprise the (L,I) value determined and updated earlier during step 320. In the illustrated example of the data set 230 in
Note that there may be more than one best value: for example, two (L,I) values may have the same lowest score. In this case, and although not illustrated, the algorithm 200 may employ tie-breaking rules at step 320 to select a single optimal (L,I) value. For example, from amongst the various potential (L,I) values that are tied, the (L,I) value with the lowest amplitude I, or the lowest energy consumption, may be selected. If more than one score is made at each of the tested values, a point discussed further below with respect to
Note that (Lopt, Iopt1), while optimized for the patient in the manner explained above, is not necessarily the best (L,I) value for the patient: some other (L,I) value not suggested as a next value by the algorithm 200, and therefore not tested, might actually correspond to a best value (e.g., lowest score S). Nevertheless, (Lopt, Iopt1) can still be said to be optimized for the patient, because the algorithm 200 searches the L,I parameter space 210 efficiently to arrive at a best value of (Lopt, Iopt1) for the patient.
At this point, the optimization algorithm 200 can determine whether the rotational angle θ at which stimulation will be applied (Lopt) should also be optimized. This depends on the determined position of Lopt, and in particular whether Lopt is proximate to split ring electrodes (370), which can require the algorithm 200 to consider the shape and placement of the electrodes on the lead. Referring again to
By contrast, if Lopt<4.0, then split ring electrodes are proximate to Lopt in this example. Note that whether Lopt is proximate to split ring electrodes can depend on the electrode configuration used to set Lopt at that longitudinal position, and whether that electrode configuration involves the use of split ring electrodes. For example, and referring to data set 230, it is seen that split ring electrodes E8-E13 are involved in setting (Lopt, Iopt1)=(1.5, 5.0), which allows the algorithm 200 to conclude that Lopt is proximate to split ring electrodes (370), because at least one split ring electrode is active to fix the position of Lopt.
When the algorithm 200 determines that Lopt is proximate to split ring electrodes (370) as in the depicted example, the algorithm 200 can proceed to determine an optimal rotational angle θopt for the application of stimulation at this longitudinal position Lopt (400), as shown in
Optimizing rotational angle θ in algorithm 200 involves trying different angles θ and amplitudes I at Lopt until θopt and Iopt2 are determined. As before, this process is iterative, and involves analogous steps (510-545) as occurred during longitudinal optimization of the stimulation (
As before, in optional step 505, certain (θ,I) values may be excluded from further consideration and testing in the algorithm 200. Such (θ,I) values may be excluded based on the factors discussed earlier in analogous step 305, such as expected ineffectiveness (e.g., as based on preliminary testing), based on objective measurements (ERNAs), upon consideration of the tissue imaging information 114, etc., although it is only necessary to consider such factors at (or near) the optimal longitudinal position (Lopt) already determined.
Referring again to
Data set 230′ keeps track of these values, and also stores (again with help of the electrode configuration algorithm) the electrode configurations needed to provide the stimulation at these different rotational locations. Data set 230′ may be a continuation of the data set 230 used during longitudinal optimization (
As before, the preset (θ,I) values are applied to the patient at Lopt (510). This causes the clinician programmer 70 to transmit stimulation parameter sets indicative of θ and Lopt (as reflected in the electrode configuration) and amplitude I to the IPG 10 so that stimulation can be produced at the prescribed angle and longitudinal position. As each of these presets (θ,I) is sequentially applied to the patient (510), at least one score S′ at each of the presets is determined (515) and entered into data set 230′ using the GUI 99. Such scores as before may be subjective or objective. Again, note in the description of rotational optimization that follows that variables are given a prime symbol (e.g., S′, R′, w′) to differentiate them from variables used earlier (
After sequentially applying stimulation according to these presets and determining and recording their scores S′ after patient testing, the programming algorithm 200 can determine a best of the (θ,I) values (θopt, Iopt2) tested to this point based on the scores S′ provided at each of the previously-tested positions (520). As the algorithm 200 iterates, more (θ,I) values will be tested and scored, and (θopt, Iopt2) can be updated accordingly at this step. At this point, after only testing the presets, (θopt, Iopt2) is determined at step 520 to be (180°, 2) (at step i=3), because this tested value yields the best (e.g., lowest) score (S′=0.3).
Next, the algorithm 200 determines whether one or more stopping criteria has been met (525), and stopping criteria can be similar to those described earlier for longitudinal optimization. If not, the algorithm 200 continues to determine a next (θ,I) value to be tested (530) in a next iteration of the algorithm (step i=5).
These details at step 530 mimic the sub-steps shown earlier in
Factors R′A, R′B, R′C, and R′Dlargely mimic their longitudinal counterparts RA, RB, RC , and RD, and comprise determinations of inverse distance (R′A), absolute distance (R′B), distance variance (R′C), and a lower amplitude preference (R′D). These factors and their resulting data sets as used during rotational optimization are not shown in the Figures, but are explained in the above-incorporated '950 Publication, including descriptions how the calculations of these factors can be varied in a rotational environment.
Tissue structure factor R′E as before is preferably determined from the tissue imaging information 114, and can be manual or automated. In either case, the clinician or an automated algorithm (e.g., 276,
As before, a weighted factor data set 299 R′W for all (θ,I) positions is determined using the factors R′A, R′B, R′C, R′D, and R′E determined earlier, which can involve the use of weights w′A, w′B, w′C, w′D. and w′E(R′W(θ,I)=w′A*R′A(θ,I)+w′B*R′B(θ,I)+w′C*R′C(θ,I)+w′D*R′D(θ,I)+w′E*R′E(θ,I)). Once R′W is determined at each of the (θ,I) values (perhaps with some values excluded), a best R′W(θ,I) value is selected from data set 299, which determines the next (θ,I) value to be tested (530). In the example shown in
Referring again to
At this point, the algorithm 200 can return to step 520, wherein a best of the tested values (θopt, Iopt2) is determined and/or updated. (θopt, Iopt2) will remain as (180 °, 2.0) (step i=3), because this step shows the best (e.g., lowest) score S′ to this point. Assuming a stopping criterium isn't met (525), the algorithm 200 continues iterating and determines a next (θ,I) value to be tested (530) (step i=6), which is applied 540 and for which a score S′ is recorded 545, etc. The effect of such successive iterations of the algorithm 200 is shown in the data set 230′ and the θ,I parameter space 220 of
If a stopping criterium has been met (525), no further (θ,I) values are determined or tested, and an optimal value of (θ,I)—(θopt, Iopt2)—is determined, which would comprise the (θ,I) value determined and updated earlier during step 520. In the illustrated example, (θopt, Iopt2) corresponds to the lowest score S′ (0.3) when θopt=180° and Iopt2=2.0 mA (step i=3). Notice in this example that (θopt, Iopt2) happens to correspond with one of the presets, but this is coincidental and wouldn't necessarily occur.
At this point, stimulation for the patient has been optimized (450), with Lopt optimized during longitudinal searching, and (if necessary, and depending on Lopt's proximity to split ring electrodes) with θopt and Iopt2 optimized during rotational searching at Lopt. To summarize, optimization algorithm 200 has determined an optimized stimulation parameter set (Lopt, θopt, Iopt2)=(1.5, 180°, 2.0 mA) for the patient.
Note that Lopt and θopt, pursuant to the electrode configuration, defines how this amplitude Iopt2=2 mA should be split between the electrodes. As shown in the data set 230′ (step i=3), the current Iopt2 should be split equally between electrodes E8, E10, E11, and E13, with each of these electrodes receiving −0.5 mA, which will place the stimulation at the optimal longitudinal (Lopt=1.5) and rotational (θopt=180°) positions relative to the lead 19. Again, other stimulation parameters—such as frequency F and pulse width PW are included as part of the optimized stimulation parameter set, which are assumed to have been optimally determined elsewhere. Like amplitude I, these other stimulation parameters could be optimized using the disclosed technique as well. As was the case with longitudinal optimization, the algorithm 200 may apply tie-breaking rules to select an optimal (θopt, Iopt2) value from between otherwise equally-valued scores S′ at step 520. Again, note that (θopt, Iopt2), while optimized for the patient in the manner explained above, is not necessarily the best (θ,I) value for the patient: some other (θ,I) value not suggested as a next value by the algorithm 200, and therefore not tested, might actually correspond to a best value (e.g., lowest score S′) for the patient. Nevertheless, (θopt, Iopt2) can still be said to be optimized for the patient, because the algorithm 200 still searches the θ,I parameter space 220 efficiently to arrive at a best value of (θopt, Iopt2) for the patient. In this sense, (θopt, Iopt2) can be said to be optimized, or comprise an optimal value, for the patient.
If the algorithm 200 determines based on Lopt that rotational optimization is recommended (see
Notice that programming algorithm 200 addresses problems of determining optimal stimulation for DBS patients. As mentioned earlier, in a typical DBS system there are many combinations of I, L, and θ that that can be tested and scored when determining optimal stimulation parameters, and testing all such combinations is burdensome and impractical during a programming session. Use of the programming algorithm 200 efficiently and automatically selects next values to test, and can automatically decide when enough values have been tested. As such, much of the guess work in selecting optimal stimulation parameters is removed, and optimal stimulation parameters can be arrived at efficiently and in a reasonable period of time, such as during a typical programming session.
Many modifications can be made to the programming algorithm 200 as described up to this point. Use of the algorithm 200 has been described as particularly useful when used to determine stimulation parameters for a patient having a directional lead (e.g., 19) with split ring electrodes at at least some longitudinal positions on the lead. With such a lead, both longitudinal optimization and rotational optimization can be useful. However, algorithm 200 may also be used in part to provide only longitudinal optimization or only rotational optimization. For example, only longitudinal optimization aspects of the technique (e.g.,
Furthermore, while the algorithm 200 has been described sequentially as comprising longitudinal optimization followed by rotational optimization, the order could be reversed. Still further, longitudinal optimization and rotational optimization can occur more than once. For example, longitudinal optimization may occur to determine Lopt1; followed by rotational optimization to determine θopt1 at Lopt1; followed by further longitudinal optimization to perhaps further optimize Lopt2 at θopt1; followed by further rotational optimization to perhaps further optimize θopt2 at Lopt2; etc.
The algorithm 200 may also be used with non-directional leads (e.g., 18,
As shown, three scores S1, S2, and S3 are determined at each step, although two or more than three could also be considered. For example, S1can comprise a bradykinesia score; S2 a rigidity score; S3 may comprise a score determined based on objective testing, such as the ERNA measurements described earlier. Again, these are just examples; other subjectively determined or objectively measured patient outcomes can be scored as noted earlier. See U.S. patent Application Publication 2021/0196956 (discussing determination of a plurality of scores for a given stimulation parameter set in a DBS application).
In Example 1, the scores S1, S2, and S3 are weighted (per weights e, f, and g) to arrive at ST, which is then used to assist in selecting a next value to be tested. Note that such weighting can comprise averaging the scores S1, S2, and S3. Once ST values are determined at previously tested locations, the algorithm 200 can proceed as before to determine a next value to be tested. Thus, and in light of scores STdetermined at earlier steps, data sets RTA(L,I) to RTE(L,I) can be determined, and these can be weighted to determine a weighted data set RW(L,I), which can be used to choose the next value ((L,I) in this case) to be tested by determining a best (e.g., lowest) value in RW(L,I).
Example 2 also involves determining a weighted data set RW(L,I) that can be used to select next values to be tested, although this doesn't use ST to do so. Instead, in this example, each of the individual scores S1, S2, and S3 are processed separately to arrive at a weighted data set associated with that score (R1W(L,I), R2W(L,I), and R3W(L,I)), and then these weighted data sets are weighted again (per e, f, and g) to arrive at data set RW. This example is beneficial because the weights used to form the RiW(L,I) data set for each score Si can be set differently if desired (e.g., wiAto wiE). Again, use of multiple scores S′ can also be used during rotational optimization to determine a next (θ,I) value to be tested.
Regardless of how RW(L,I) is determined and used to select next points to be tested, the algorithm 200 can use any of the scores to ultimately select optimal stimulation parameters (in this case, Lopt and Iopt1). For example, once data set 230 is complete and all values have been tested, STcan be assessed to determine Lopt and Iopt1 in this example, which may make sense as ST comprises a general averaging of the individual scores S1, S2, and S3. If ST is used in this manner, the algorithm 200 would determine that Lopt=3.2, and Iopt1=1.2 mA (step i=8), because this step corresponds to the best (lowest) value for ST(0.3) in data set 230. Alternatively, the algorithm 200 could use any of the individual scores in data set 230 to determine optimal parameters. Assume for example that S1 scores a particularly important symptom such as bradykinesia. The algorithm 200 may thus use this score S1to determine the optimal parameters. If S1 is used in this manner, the algorithm 200 would determine that Lopt=1.5, and Iopt1=5.0 mA (step i=7), because this step corresponds to the best (lowest) value for S1 (0.4) in data set 230. ST by contrast may be used only to assist in selecting the next values to be tested, or may not be used at all. Still alternatively, algorithm 200 could assess all scores S1, S2, S3, and ST, and use the best (lowest) value of all of these to select the optimal parameters.
Although particular embodiments of the present invention have been shown and described, it should be understood that the above discussion is not intended to limit the present invention to these embodiments. It will be obvious to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the present invention. Thus, the present invention is intended to cover alternatives, modifications, and equivalents that may fall within the spirit and scope of the present invention as defined by the claims.
This is a non-provisional application of U.S. Provisional Patent Application Ser. No. 63/483,645, filed Feb. 7, 2023, which is incorporated herein by reference and to which priority is claimed.
| Number | Date | Country | |
|---|---|---|---|
| 63483645 | Feb 2023 | US |
