Source: http://www.google.com/patents/US20090018609?ie=ISO-8859-1&dq=inventor:%22Arthur+R.+Hair%22
Timestamp: 2014-07-30 09:06:57
Document Index: 30733962

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60']

Patent US20090018609 - Closed-Loop Feedback-Driven Neuromodulation - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a �nervous system component.� The neurological control system generates neural modulation...http://www.google.com/patents/US20090018609?utm_source=gb-gplus-sharePatent US20090018609 - Closed-Loop Feedback-Driven NeuromodulationAdvanced Patent SearchPublication numberUS20090018609 A1Publication typeApplicationApplication numberUS 12/177,060Publication dateJan 15, 2009Filing dateJul 21, 2008Priority dateAug 5, 1998Publication number12177060, 177060, US 2009/0018609 A1, US 2009/018609 A1, US 20090018609 A1, US 20090018609A1, US 2009018609 A1, US 2009018609A1, US-A1-20090018609, US-A1-2009018609, US2009/0018609A1, US2009/018609A1, US20090018609 A1, US20090018609A1, US2009018609 A1, US2009018609A1InventorsDaniel John DiLorenzoOriginal AssigneeDilorenzo Daniel JohnExport CitationBiBTeX, EndNote, RefManReferenced by (2), Classifications (13), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetClosed-Loop Feedback-Driven NeuromodulationUS 20090018609 A1Abstract A neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a �nervous system component.� The neurological control system generates neural modulation signals delivered to a nervous system component through one or more neuromodulators, comprising intracranial (IC) stimulating electrodes and other actuators, in accordance with treatment parameters. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals sensed by one or more sensors, each configured to sense a particular characteristic indicative of a neurological or psychiatric condition.
20. The system of claim 1, wherein said control law is model-based, wherein a computer-based dynamic model of the system being controlled is continuously adjusted according to a function of the difference between the model state and the measured state, and whereby the state of the model is then used to determine the control signal fed to the actual controlled system. Description
CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. application Ser. No. 11/138,621, filed May 25, 2005 (now U.S. Pat. No. 7,403,820); which application is a continuation-in-part of U.S. application Ser. No. 10/889,844, filed Jul. 12, 2004 now U.S. Pat. No. 7,231,254); which claims benefit of U.S. Provisional Application No. 60/562,487, filed Apr. 14, 2004; U.S. application Ser. No. 10/889,844 is also a continuation-in-part of U.S. application Ser. No. 10/818,333, filed Apr. 5, 2004 (now U.S. Pat. No. 7,277,758); which claims benefit of U.S. Provisional Application No. 60/460,140, filed Apr. 3, 2003; U.S. application Ser. No. 10/818,333 is also a continuation-in-part of U.S. application Ser. No. 10/753,205, filed Jan. 6, 2004 (now U.S. Pat. No. 7,242,984); which claims the benefit of U.S. Provisional Application No. 60/438,286, filed Jan. 6, 2003; U.S. application Ser. No. 10/753,205 is also a continuation-in-part of U.S. application Ser. No. 10/718,248, filed Nov. 20, 2003 (now U.S. Pat. No. 7,209,787); which is a continuation-in-part of U.S. application Ser. No. 10/008,576, filed Nov. 11, 2001 (now U.S. Pat. No. 6,819,956); which is a continuation-in-part of U.S. application Ser. No. 09/340,326, filed Jun. 25, 1999 (now U.S. Pat. No. 6,366,813); which claims the benefit of U.S. Provisional Application No. 60/095,413, filed Aug. 5, 1998; U.S. application Ser. No. 10/718,248 also claims benefit of U.S. Provisional Application No. 60/427,699, filed Nov. 20, 2002, and claims benefit of U.S. Provisional Application No. 60/436,792, filed Dec. 27, 2002. All of these applications are incorporated herein by reference as if fully set forth herein.
SUMMARY OF THE INVENTION The present invention is a neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a �nervous system component.� The neurological control system generates neural modulation signals delivered to a nervous system component through one or more intracranial (IC) stimulating electrodes in accordance with treatment parameters. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals sensed by one or more sensors, each configured to sense a particular characteristic indicative of a neurological or psychiatric condition. Neural modulation signals include any control signal that enhances or inhibits cell activity. Significantly the neurological control system considers neural response, in the form of the sensory feedback, as an indication of neurological disease state and/or responsiveness to therapy, in the determination of treatment parameters.
DETAILED DESCRIPTION OF THE INVENTION FIG. 1 is a schematic diagram of one embodiment of the intracranial stimulator of the present invention implanted bilaterally in a human patient. In the embodiment illustrated in FIG. 1, two neurological control systems 999 are shown implanted bilaterally. Each system 999 includes a stimulating and recording unit 26 and one or more intracranial components described below. As described in this illustrative embodiment, the intracranial components preferably include a stimulating electrode array 37. However, it should become apparent to those of ordinary skill in the relevant art after reading the present disclosure that the stimulating electrodes may also be extracranial; that is, attached to a peripheral nerve in addition to or in place of being located within the cranium. As shown in FIG. 1, stimulating and recording unit 26 of each neurological control system 999 is preferably implanted contralateral to the intracranial components of the device.
In the illustrative embodiment, disease state estimator module array 229 includes an EMG signal processor 233, EEG signal processor 234, accelerometer signal processor 235, acoustic signal processor 236, peripheral nerve electrode (PNE) signal processor 237, intracranial recording electrode (ICRE) signal processor 238, and intracranial stimulating electrode (ICSE) signal processor 239. It should be understood that other signal processors may also be included in the array 229. Inputs to these modules include conditioned EMG signal path 78, conditioned EEG signal path 79, conditioned accelerometer signal path 80, conditioned acoustic signal path 81, conditioned peripheral nerve electrode (PNE) signal path 82, conditioned intracranial recording electrode (ICRE) signal path 83, and conditioned intracranial stimulating, electrode (ICSE) signal path 84, respectively. Communication between these modules is facilitated. The output(s) of each of the modules is connected to an aggregate disease state estimator 195. Aggregate disease state estimator 195 generates a single or plurality of disease state estimates �X� indicative of state of disease and response to treatment.
u 3 = ∫ - w / 2 w / 2  X EMG -   t ( 3 ) over a given time window −w/2 to +w/2. A simplified representation of this is given by the equation:
−C 1 ∂u 4 /dt+C 2 �u 4 =B 1 �∂X EMG /dt+B 2 �X EMG (4)
u(s)/X EMG(s)=(B 1 �s+B 2)/(C 1 �s+C 2) (6)
H(s)=u(s)/X EMG(s)=(B 1 �s+B 2)/(C 1 �s+C 2) (7)
H(s)=u(s)/X EMG(s)=G V/EMG(0.1�s+1)/(2�s+1) (8)
DSI1=RTN Where Normalized Resting Tremor Magnitude RTN is given by:
RTN =T A,3-5 *W TA,3-5 +T E,3-5 *W TE,3-5 +T P,3-5 *W PE,3-5 +T C,3-5 +W TC,3-5 +T N,3-5 *W TN,3-5 +T S,3-5 *W TS,3-5 +T E,3-5 *W TE,3-5 Where the factors from which the Resting Tremor Magnitude RTN is determined, representing estimates of the magnitude of 3-5 Hertz movement of selected body segments, including but not limited to limbs, torso, and head are:
 T A , 3  -  5 = Tremor   level   determined   by   acceleration   monitoring  W TA , 3  -  5 = Weighting   factor   for   tremor   T A , 3  -  5 T E , 3  -  5 = Tremor   level   determined   by   electromyographic   ( EMG )   monitoring  W TE , 3  -  5 = Weighting   factor   for   tremor   T E , 3  -  5 T P , 3  -  5 = Tremor   level   determined   by   peripheral   nerve   electrode   monitoring  W TP , 3  -  5 = Weighting   factor   for   tremor   T P , 3  -  5 T C , 3  -  5 = Tremor   level   determined   by   cortical   electrode   monitoring  W TC , 3  -  5 = Weighting   factor   for   tremor   T C , 3  -  5 T N , 3  -  5 = Tremor   level   determined   by   neural   monitoring , including   subcortical   nuclei , white   matter   tracts , and   spinal   cord   neurons  W TN , 3  -  5 = Weighting   factor   for   tremor   T N , 3  -  5  T S , 3  -  5 = Tremor   level   determined   by   acoustic   sensor   monitoring  W TS , 3  -  5 = Weighting   factor   for   tremor   T S , 3  -  5 Weighting factors are adjusted after implantation to achieve normalization of RTN and to allow for selective weighting of tremor levels as determined from signals arising from various sensors, including but not limited to those listed.
DSI2=DN Where Normalized Dyskinesia Magnitude DN is given by:
D N = D A * W DA + T E * W TE + T P * W PE + T C + W TC + T N * W TN + T S * W TS + T E * W TE  Where  D A , 3 - 5 = Dyskinesia   level   determined   by   acceleration   monitoring  W DA , 3 - 5 = Weighting   factor   for   Dyskinesia   D A , 3  -  5 D E , 3  -  5 = Dyskinesia   level   determined   by   electromyographic   ( EMG )   monitoring  W DE , 3  -  5 = Weighting   factor   for   Dyskinesia   D E , 3  -  5 D P , 3  -  5 = Dyskinesia   level   determined   by   peripheral   nerve   electrode   monitoring  W DP , 3  -  5 = Weighting   factor   for   Dyskinesia   D P , 3  -  5 D C , 3  -  5 = Dyskinesia   level   determined   by   cortical   electrode   monitoring  W D   C , 3  -  5 = Weighting   factor   for   Dyskinesia   D C , 3  -  5 D N , 3  -  5 = Dyskinesia   level   determined   by   neural   monitoring , including   subcortical   nuclei , white   matter   tracts , and   spinal   cord   neurons  W DN , 3  -  5 = Weighting   factor   for   Dyskinesia   D N , 3  -  5 D S , 3  -  5 = Dyskinesia   level   determined   by   acoustic   sensor   monitoring  W DS , 3  -  5 = Weighting   factor   for   Dyskinesia   D S , 3  -  5 The third intrinsic disease state DSI3 represents the level of rigidity.
DSI3=RN Where Normalized Rigidity Magnitude RN is given by:
R N = R A * W RA + R E * W RE + R P * W RE + R C + W RC + R N * W RN + R S * W RS + R E * W RE  Where  R A , 3  -  5 = Rigidity   level   determined   by   acceleration   monitoring  W RA , 3  -  5 = Weighting   factor   for   Rigidity   R A , 3  -  5 R E , 3  -  5 = Rigidity   level   determined   by   electromyographic   ( EMG )   monitoring  W RE , 3  -  5 = Weighting   factor   for   Rigidity   R E , 3  -  5 R P , 3  -  5 = Rigidity   level   determined   by   periheral   nerve   electrode   monitoring  W RP , 3  -  5 = Weighting   for   Rigidity   R P , 3  -  5 R C , 3  -  5 = Rigidity   level   determined   by   cortical   electrode   monitoring  W RC , 3  -  5 = Weighting   factor   for   Rigidity   R C , 3  -  5 R N , 3  -  5 = Rigidity   level   determined   by   neural   monitoring , including   subcortical   nuclei , white   matter   tracts , and   spinal   cord   neurons  W RN , 3  -  5 = Weighting   factor   for   Rigidity   R N , 3  -  5  R S , 3  -  5 = Rigidity   level   determined   by   acoustic   sensor   monitoring  W RS , 3  -  5 = Weighting   factor   for   Rigidity   R S , 3  -  5 The fourth intrinsic disease state DSI4 represents the level of bradykinesia.
DSI1=BN Where Normalized Bradykinesia Magnitude BN is given by:
BE Where RA=Bradykinesia level determined by acceleration monitoring
WRC=Weighting factor for Bradykinesia RC RN=Bradykinesia level determined by neural monitoring, including subcortical nuclei,
WRS=Weighting factor for Bradykinesia RS The control law drives these disease states toward their reference values, nominally 0, according to a vector of weights, establishing a prioritization.
One advantage of the present invention is that it provides prediction of future symptomatology, cognitive and neuromotor functionality, and treatment magnitude requirements. Such predictions may be based on preset, learned and real-time sensed parameters as well as input from the patient, physician or other person or system. The prediction of future symptomatology is based upon any of several weighted combination of parameters. Based upon prior characterization of the circadian fluctuation in symptomatology (that is, tremor magnitude for deep brain stimulation or level of depression for stimulation of other sites including locus ceruleus), future fluctuations may be predicted. An estimate, or model, of fluctuation may be based upon a combination of preset, learned, and real-time sensed parameters. Preset parameters are derived from clinical studies designed specifically for the purpose of gathering such data, or from estimates extracted from data gleaned from published literature. Real-time sensed parameters are derived from the current states (and changes, i.e. derivatives and other processed signals, thereof) of sensed and processed signals. Learned parameters are based upon the time histories of previously sensed signals. For example, the circadian fluctuation in tremor amplitude may be sensed; a weighted average of this data collected over numerous prior days provides as estimate of the expected tremor amplitude as well as a standard deviation and other statistical parameters to characterize the anticipated tremor amplitude. Similarly, in the presence of closed-loop feedback, the level of stimulation required to reduce or eliminate tremor may be used as an estimate of the �amplitude� or state of the underlying disease.
Another advantage of the present invention is that it performs automated determination of the optimum magnitude of treatment by sensing and quantifying the magnitude and frequency of tremor activity in the patient, a quantitative representation of the level or �state� of the disease is determined. The disease state is monitored as treatment parameters are automatically varied, and the local or absolute minimum in disease state is achieved as the optimal set of stimulation parameters is converged upon. The disease state may be represented as a single value or a vector or matrix of values; in the latter two cases, a multivariable optimization algorithm is employed with appropriate weighting factors.
Fuzzy Control: �Fuzzy� rule based control is a control scheme that enables continuous variables to be characterized in a nondiscrete manner into multiple categories. Fuzzy Control systems have 3 stages: (1) an input stage that maps the input signal into membership values for each of a set of groups, (2) processing stage consisting of a singular or multiplicity of rules, and (3) and output stage that combines the rule outputs into s precise control output value. In Fuzzy Control, an input variable, such as Neural State X(t), is characterized by its degree of membership in a collection of partially overlapping groups. This is accomplished by assigning a �weighting� which describes the degree of �membership� of the variable into each category. The membership in each category varies between 0 and 1 and may be continuous. This offers the fundamental benefit of avoiding arbitrary thresholds and consequent discontinuous or erratic behavior that a simple threshold based controller, such as a bang-bang controller, might exhibit. This is depicted in FIG. 16, wherein Neural State X is shown to vary between −100 and +100, and this range is divided into 5 overlapping ranges: Low Abnormal (A), Low Normal (B), Normal (C), High Normal (D), and High Abnormal (E).
Each of one or more component continuous variables x comprising input Neural State X is transformed into weightings or memberships in a set of so-called fuzzy membership functions M(X)={A(X), B(X), C(X), D(X), E(X)}, exhibiting mutual overlap between membership functions. Control is accomplished by application of sets of rules applied on any singularity or multiplicity of membership functions. An example of a 2 dimensional fuzzy controller is: X1 membership=M(X1) {High Stimulus Level, Medium Stimulus Level, Low Stimulus Level} and X1 membership=M(X2)={High Symptoms, Medium Symptoms, Low Symptoms}. For example rule 1 might be �If X1=High Symptoms AND X2=Low Stimulus Level, THEN High Stimulus Level�. Rule 2 might then be �If X1=Medium Symptoms AND X2=Low Stimulus Level, THEN Medium Stimulus Level�. For a 3 dimensional system, a Rule 3 could then be �If X1=High Symptoms AND X2=High Stimulus Level AND X3−Medium Stimulus Frequency, THEN High Stimulus Frequency�. For each rule, a �truth value� is calculated based upon the input values of the memberships and their Boolean relation. Rule 1, �If X1=High Symptoms AND X2=Low Stimulus Level, THEN High Stimulus Level�, could become M(X1, High Symptoms) AND M(X2, Low Stimulus Level), which is the minimum of the two membership values. If the Boolean operator were �OR�, then the truth value would be the maximum of the two membership values.
Referenced byCiting PatentFiling datePublication dateApplicantTitleUS8688222 *Feb 5, 2009Apr 1, 2014Cochlear LimitedStimulus timing for a stimulating medical deviceWO2011126580A2 *Apr 11, 2011Oct 13, 2011Minnow Medical, Inc.Power generating and control apparatus for the treatment of tissue* Cited by examinerClassifications U.S. Classification607/45, 607/59International ClassificationA61N1/36, A61N1/08, H03C3/06Cooperative ClassificationA61N1/36064, A61N1/36067, A61N1/0529, A61N1/3605, A61N1/36082European ClassificationA61N1/36Z, A61N1/36E, A61N1/36Legal EventsDateCodeEventDescriptionApr 10, 2013ASAssignmentFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:NEUROVISTA CORPORATION;REEL/FRAME:030192/0408Effective date: 20130228Owner name: CYBERONICS, INC., TEXASSep 14, 2012ASAssignmentFree format text: SECURITY AGREEMENT;ASSIGNOR:NEUROVISTA CORPORATION;REEL/FRAME:028959/0395Owner name: CYBERONICS, INC., TEXASEffective date: 20120914Oct 1, 2008ASAssignmentOwner name: BIONEURONICS CORPORATION, WASHINGTONFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DILORENZO, DANIEL JOHN;REEL/FRAME:021616/0042Effective date: 20050801Owner name: NEUROVISTA CORPORATION, WASHINGTONFree format text: CHANGE OF NAME;ASSIGNOR:BIONEURONICS CORPORATION;REEL/FRAME:021616/0248Effective date: 20070501RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google