WO2008109508A2 - Sélection automatique de paramètres, et réglage thérapeutique permettant d'accroître l'efficacité des thérapies utilisant un dispositif neuronal réactif pour maladies et lésions cérébrales - Google Patents

Sélection automatique de paramètres, et réglage thérapeutique permettant d'accroître l'efficacité des thérapies utilisant un dispositif neuronal réactif pour maladies et lésions cérébrales Download PDF

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WO2008109508A2
WO2008109508A2 PCT/US2008/055629 US2008055629W WO2008109508A2 WO 2008109508 A2 WO2008109508 A2 WO 2008109508A2 US 2008055629 W US2008055629 W US 2008055629W WO 2008109508 A2 WO2008109508 A2 WO 2008109508A2
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therapy
patient
effectivity
neurological disorder
features
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PCT/US2008/055629
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WO2008109508A3 (fr
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Brian Litt
Javier R. Echauz
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The Trustees Of The University Of Pennsylvania
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36064—Epilepsy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36082—Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease

Definitions

  • the present invention is in the field of medical devices to treat neurological disorders of the brain. More specifically, the invention aims at a method to automatically select the character, dosage parameters and the timing of therapy delivery in an on- demand device with the goal of increasing efficacy in the treatment of epilepsy as well as other neurological and psychiatric disorders, where parameter autotuning, (automatically adapting to individual patients, performance criteria, and patterns), and therapy timing may be beneficial.
  • a neuro-device Unlike profound sensorineural hearing loss, which can remain stable over time, epilepsy, migraine, recurrent depression, pain, bipolar-disorder, anxiety-, sleep-, abnormal movements-, and other central and peripheral nervous system disorders often display paroxysmal onset and offset of disruptive events. In such cases, it is advantageous for a neuro-device to utilize event detection in order to deliver therapy only at times when it is needed, just before or during the disruptive events, thereby conserving battery life (if battery powered) and reducing unnecessary potential side effects, such as effects of continuous intervention on brain tissue.
  • VNS vagal nerve stimulation
  • DBS deep brain stimulation
  • TMS transcranial magnetic stimulation
  • EEG intracranial EEG is sensed and optionally stored, allowing one to program an automatic detector for epileptiform activity and use it to trigger stimulation at the approximate times when such therapy should be beneficial.
  • Methods for detecting neurological events from brain signals are well known.
  • Methods for fine-tuning the parameters of such event detectors are also known in the field and art.
  • U.S. Patent No. 6,161,045 to Fischell et al. discloses the use of electrically evoked epileptiform activity during presurgical EEG monitoring and brain mapping in order to obtain stimulation parameters for use in a subsequently implanted responsive neurostimulator. This method is not applicable to tuning stimulation parameters as is most frequently needed, during post-implantation and long-term use.
  • an automatic fully responsive neurotherapy system that recognizes and automatically select efficacious therapy parameters, and to finely manipulate the on-off timing of therapy delivery aimed at improving efficacy over the entire lifespan of the system.
  • the system incorporates autotuning of the character, dose, and timing of neurotherapy across patients, and on-the-fly, on a patient- specific, event- by-event basis.
  • Significant advantages over prior systems can be achieved by leveraging neurotechnology through "pre-" and "post-stimulation effectivity" features derived from the biological signal sensing and seizure detection subsystems.
  • a self-organizing "therapy rulebase” is developed to make sense of the "effectivity” features, and subsequently command their trajectories in directions of clinical improvement.
  • the system and method work in effect as a multistart stochastic optimization algorithm aimed at maximizing neurotherapy efficacy and clinical benefit.
  • FIG. 1 is an example of a block diagram of neuro therapy device or system that performs automatic therapy parameter selection.
  • FIG. 2 is a diagram illustrating the relationship between the flow charts of FIGs. 3, 4 and 7 that together depict a method for automatic therapy parameter selection.
  • FIG. 3 is a flowchart of a method for learning general seizure (SZ) onset and stereotypical seizure onset.
  • FIG. 4 is a flowchart that depicts a method of creating an initial therapy rulebase.
  • FIG. 5 illustrates an example of a stylized plot of a brain signal showing a pre- stimulation period (a), a stimulation period (b), a stimulation artifact period (c)
  • FIG. 6 shows an example of an EEG signal, Hilbert-based instantaneous phase, and the dependence of optimal therapy timing on the critical phase range.
  • FIG. 7 is a flowchart that depicts a method of learning efficacious therapy parameters online and applying them on-the-fly.
  • FIG. 8 is an example of a more detailed block diagram that also shows interaction of the major functional components of the system shown in FIG. 1 during normal therapy mode.
  • the method involves sensing at least one biological condition indicative of neurological or other biological activity in a patient and generating a sensor signal representative thereof; analyzing the sensor signal to detect occurrence of a neurological disorder event and to identify at least one stereotypical onset pattern associated with the neurological disorder event; storing therapy rulebase data that maps real-time conditions of the patient to at least one parameter for at least one therapy to be applied to the patient; and automatically selecting parameters for a therapy for the patient based on the sensor signal and therapy rulebase data.
  • the system comprises a biological sensor subsystem (comprising at least one biological sensor) that is configured to sense at least one biological condition indicative of neurological activity in a patient and to generate a sensor signal representative thereof; a therapy application subsystem (comprising at least one therapy applicator) that is configured to deliver at least one therapy to the patient in order to treat a neurological disorder event in the detected in the patient; and a controller coupled to the at least one biological sensor and to the at least one therapy applicator.
  • a biological sensor subsystem comprising at least one biological sensor
  • a therapy application subsystem comprising at least one therapy applicator
  • the controller is configured to: analyze the sensor signal to detect occurrence of a neurological disorder event in the patient and to identify at least one stereotypical onset pattern associated with the neurological disorder event; store therapy rulebase data that maps real-time conditions of patient to at least one parameter for a least one therapy to be applied to the patient by the therapy application subsystem; and automatically select parameters for a therapy for the patient based on the sensor signal and the therapy rulebase data.
  • a neurotherapy device or system is shown at 1000.
  • the system comprises a neurotherapy controller subsystem 1100, a biological sensor subsystem 1200 and a therapy applicator subsystem 1300.
  • the controller subsystem 1100 is coupled to the sensor subsystem 1200 and the therapy applicator subsystem 1300.
  • at least a portion (if not all) of the subsystems 1100, 1200 and 1300 are configured to be contained in appropriate housings so that they can be implanted within and/or attached to the body of a patient.
  • there is a communication interface 1400 that is connected to the controller subsystem 1100 to allow for two-way (wired or wireless) communication between the system 1000 and a computing device shown at 2000.
  • the controller subsystem 1100 may be one or any combination of a processing device, such as a microprocessor or microcontroller that executes one or more software or computer programs stored in computer readable medium, a digital signal processor, an application specific integrated circuit (ASIC), or any other combination of programmable or fixed logic devices. It is understood that there may be analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) in the system 1000 to facilitate signal analysis in the digital domain, and supply of therapy control signals to the analog domain, if necessary.
  • ADCs analog-to-digital converters
  • DACs digital-to-analog converters
  • the computing device 2000 has a communication interface 2100 that transmits signals to, and receives signals from, the communication interface 1400 in the system 1000.
  • a medical profession or other trained person may interact with the system 100 via the computing device 2000 using a display and user interface equipment shown at 2200.
  • the computing device 2100 may be an off-the-shelf computer (hand-held, laptop, desktop, etc.) or a customized computing device configured to interact with the system 1000.
  • the biological sensor subsystem 1200 comprises one or more sensors that are configured to sense any biological signal that represents or indicates neurological activity of a patient.
  • a sensor is an electroencephalogram (EEG) sensor.
  • EEG electroencephalogram
  • Other sensors and conditions that may be monitored include, without limitation: cardiac signals, neural activity from other brain structures, nuclei or functional regions associated with other neurological disorders such as depression, mania, pain, schizophrenia; systems that control bodily movement, either of the limbs, trunk, head and neck, or internal movement such as peristalsis.
  • Other conditions that could be monitored are those that generate neural or other signals, chemical, growth or change in composition or physical characteristics. Examples include stomach secretion of acid, release of neurotransmitters, rate of growth of tissue such as peripheral nerves after injury, tumor growth or reduction, etc.
  • the therapy applications subsystem 1300 comprises one or more therapy application devices that can treat a neurological disorder event in a patient.
  • a therapy application device that can treat a neurological disorder event in a patient.
  • IPG implantable pulse generator
  • Other types of therapy applicators include, without limitation, drug delivery devices such as those that release antiepileptic or neuromodulatory substances in the brain to treat abnormal neural activity, mood, behavior, awareness, etc., devices that release chemicals, drugs and other substances or electrical stimulation in the heart, limbs, viscera, peripheral nerves, blood vessels or other structures.
  • the system might also accommodate devices that record and/or deliver light to modulate light-activated drugs, substances or other forms of therapy.
  • a medicine-refractory patient typically has electrodes implanted on or near epileptic brain foci as part of a presurgical evaluation in the hope of capturing and localizing activity delineating the epileptic network.
  • an external neurostimulation device is used to determine if regions of seizure onset overlap regions of eloquent cortex whose surgical removal could cause significant functional (cognitive, motor, sensory, emotional, psychiatric etc.) impairment.
  • an implanted neurostimulator is a viable therapeutic option, even in those patients who are not surgery candidates, because interventions can be turned on, off, and modified at will, via software.
  • Brain mapping generates some experimental data regarding current intensity and frequency levels for which epileptiform after-discharges are evoked or unpleasant sensations are reported by the patient in various locations. These thresholds may vary from location to location, depending upon characteristics of the brain underlying stimulation electrodes, and the electrode-tissue interface.
  • neurological disorder event is used herein and it meant to include, without limitation, the aforementioned neurological diseases described above, as well as abnormal or undesired movements, behavior, pain, confusion, cognitive dysfunction, loss of awareness, coma, syncope, or similar events caused by dysfunction in other organs, such as heart, lungs, kidneys, liver etc.
  • FIG. 2 shows that the method for automatic therapy parameter selection is depicted by the flow charts (connected to each other by appropriate connector labels) of FIGs. 3, 4 and 7.
  • FIGs. 3 and 4 illustrate aspects of the method that pertain to learning modes in order to develop a stereotypical onset discriminator, data for a stereotypical therapy rulebase, and improvement-seeking therapy profiles.
  • the system can engage in "normal" therapy mode, where the processing shown in FIG. 7 occurs every time there is a therapy, or on demand by a physician upon interrogation of the system.
  • the trapezoidal boxes indicate that some degree of manual input/human intervention may be performed in that step.
  • therapy parameters of the therapy application subsystem 1300 are initialized for a newly implanted system 1000 in the patient.
  • the therapy application subsystem 1300 may comprise an IPG.
  • Numerous techniques are known for initializing the stimulation parameters, examples of which are disclosed in aforementioned U.S. Patent No. 6,161,045 to Fischell et al. These parameters are saved, but at 20 the therapy application subsystem 1300 itself is initially disabled, since therapy delivery is to be contingent upon the detection of epileptiform events, yet to be determined.
  • electrode impedances may fluctuate due to hemorrhage, edema, or other response to surgical implantation.
  • a monitoring period is devoted to tuning seizure detection or prediction parameters that are appropriate for the particular patient.
  • initial parameters are set on the sensitive rather than on the specific end of the spectrum, using a universal seizure detector such as linelength, or a combination of useful quantitative features, in order to avoid missing seizures.
  • three or more EEGs are automatically captured by the device, with the onset of epileptiform EEG activity occurring one or more minutes into each recording.
  • these raw EEG signal epochs are telemetered from the implanted device to a permanent, cumulative EEG archive 50 such as a database located remotely. This may require transmission to an interim receiver, either mobile with the patient or stationary, and later transmission to a central processing center.
  • Archiving EEG data allows offline analyses and ensures minimal loss of data as the device begins overwriting previous recordings due to limited storage capacity.
  • Tuning of seizure detection or prediction parameters preferably involves evaluation of a performance metric 70, and iterations of the steps 40 through 90 in FIG. 3, until a desired blend of sensitivity and specificity or receiver operating characteristic point are achieved.
  • Other biological signals related to neurological activity that are detected by the biological sensor subsystem 1200 may also be processed in a manner similar to that shown in FIG. 3.
  • a stereotypical onset pattern discriminator function is provided. It is well known that seizure onset patterns within an individual are often stereotyped, though a single patient may have several different stereotyped onsets, depending upon the nature of his/ her disorder. Meta-analyses of published studies suggest an average 60% of all within-patient seizures may be similar, possibly reflecting a stable unitary pathophysiological substrate for a patient's most frequent kind of seizure. There are several advantages associated with the stereotypical onset pattern discriminator 60. First, it is expected that the learning of efficacious therapy parameters for the stereotypical onset will converge since the "consistent" problem source can afford a consistent therapeutic solution (while the tuning of parameters for the non-stereotypical class may need to continue indefinitely).
  • stereotypic onsets may sometimes require different responses due to changes in patient environment or circumstances, such as missing seizure medications, exposure to seizure-inducing substances (e.g. alcohol, other medications) or conditions (sleep deprivation, stress etc.).
  • seizure-inducing substances e.g. alcohol, other medications
  • conditions e.g. stress, stress etc.
  • the stereotypical onset discriminator function is a post- detector for the universal seizure detector developed in steps 40 through 90 shown in FIG. 3. For only those EEG segments that have been flagged as "seizure" by the detector, input features such as linelength are further examined in order to separate stereotypical from non-stereotypical onset classes.
  • a separate dedicated classifier can be built to be run in parallel with the seizure detector. In both cases, the captured electrograms may be expertly labeled as stereotypical vs. non-stereotypical in order to form a training set upon which tuning of the discriminator parameters is based during an offline session.
  • a stereotypical onset pattern is determined at 60
  • an evaluation is made at 70 as to the performance of the detection of a stereotypical onset. Then, at 80, it is determined whether the detection performance was acceptable (within some predetermined performance ranges of thresholds). If the performance was determined not to be acceptable, then at 90, the seizure detection parameters as well as the stereotypical onset pattern discriminator are tuned accordingly.
  • Step 120 defines extraction of "pre-therapy and post-therapy effectivity” features, also referred to as effectivity features hereinafter. These features are EEG signatures extracted peri-ictally, just prior to and just after therapy, such that the acute effects of the intervention can be quantified.
  • an effectivity feature is any observable parameter, measure or quantifiable phenomenon, which may be associated with device efficacy.
  • each therapy is triggered by a seizure detection that occurred less than 400 ms earlier.
  • the seizure detection time also forms a natural fiduciary point for comparisons with non-stimulated (but nevertheless detected) events.
  • pre- therapy features are predictive of seizure termination
  • post-therapy features in non-terminated seizures show nevertheless reduced seizure severity compared to post-detection features in non- stimulated seizures
  • paired post-therapy to pre-therapy features show a "normalizing" effect on EEG compared to paired post-detection to pre-detection features (ratios) in non-treated events, etc.
  • Therapy artifacts can interfere with EEG recording, so it is customary to start observation of post-event features about 2 seconds after event onset time as shown in FIG. 5.
  • the character of the pre-effectivity features is the same as that of the post-effectivity features.
  • a pre-therapy linelength compared to a post- therapy linelength is still a linelength, the only difference lying on temporal location with respect to the fiduciary point.
  • different post-event feature characters may be useful, for example, when measuring seizure spread across channels, which is undefined pre-ictally (although this is derivable as channel count of off-limit energy or cross- channel sum of energies — a common pre-effectivity feature). Accordingly, this scheme can be used to control changes in epileptic networks that reliably precede seizures, providing a means to arrest seizure generation prior to actually ictal onset events.
  • seizure “precursor” events can be measured multi-scale, and may come from a variety of sensors measuring noise, unit and multi-unit activity (at the cellular level) and extend down to wider-spread field potentials in a variety of bandwidths, down to DC range activity.
  • effectivity features include brain signal energy, mean frequency, rhythmicity, complexity, high-frequency oscillation, terminal phase, in-phase synchronization, phase locking, network phase distribution, and cross-channel global correlation.
  • effectivity features include “clock” variables such as time of day, day of menstrual period, etc., associated with ultra-, infra-, and circadian rhythms and their relation to device efficacy for certain nocturnal/diurnal/awakening and catanienial epilepsies.
  • Still further examples of effectivity features include "external trigger” variables such as light, noise, music, text, sleep, oxygen, food, serum glucose, fluctuations in hormone or other measurable concentrations in serum, other bodily fluids or by non-invasive means etc.
  • Some measure parameters may involve measuring stimuli such as light/ strobe exposure, touch, sound, cognitive tasks and other stimuli associated with device efficacy in certain reflex epilepsies.
  • Pathological brain states are associated with extreme brain signal energies (e.g., higher ictally, lower postictally), extreme frequencies (e.g., slowed background, chirping overshoot ictally), higher rhythmicity/periodicity, extreme complexities (e.g., higher linelength at onset, lower fractal dimension ictally), certain high-frequency oscillation patterns, certain ranges of instantaneous phase values at the time of therapy (e.g., lower vulnerability of synchronized state, contributing to ineffective therapy), higher in-phase synchronizations between two channels, higher phase difference locks between two channels, higher peakedness in the phase distributions of network oscillators, higher global correlations across spatially distinct channels, certain hours of the day or night, certain days of the month, certain photic frequencies, higher levels of noise, certain spectral content of music, certain words being read, sleep deprivation, hyperventilation, and consumption of certain foods. Effectivity features make it apparent when the brain enters in and out of pathological states (a
  • the biological sensor signal represents brain function of the patient
  • the signal may analyzed for linelength features to derive at least one of instantaneous amplitude, frequency and phase for the electrical stimulation.
  • Linelength has been used in the lie detection, image processing, and seizure detection arts as a simple, generic "motion detector.” Not generally known, as disclosed herein, is the fact that linelength can also be used in implantable devices to monitor instantaneous frequency and phase of a biological signal.
  • the amplitude has to vary very slowly compared to the oscillation.
  • Relative changes in phase of the signal can be tracked with the integral of the instantaneous frequency estimate.
  • the captured treated events are uploaded to the EEG archive 50) and the effectivity features are calculated initially offline at 120. All of this information may be made available for human review on a computer or hand-held device.
  • neurologists or other expertly trained personnel can then score the treated events in terms of the acute efficacy of the therapy, as seen on EEG and optionally aided by post-effectivity quantities.
  • the scoring consists of annotating each treated event as “successful” vs. "unsuccessful,” where success is defined as seizure termination within, e.g., 5 seconds of the onset of therapy.
  • the expert scorer further stratifies or classifies each stimulated event into either stereotypical or non- stereotypical seizure onset. These further samples of onset types can be added to the discriminator 60 training set in order to refine its parameters for its upcoming unsupervised, online application.
  • events that are classified as non- stereotypical events are stored in an archive. From this point forward, the focus of the system and method described herein is on learning efficacious therapy parameters for seizures of the stereotypical class.
  • a stereotypical therapy archive 160 The collection of pre- and post-therapy effectivity features, augmented with then- in-effect therapy parameters, along with the initially manually scored therapy outcomes, is organized in a table or data structure called a stereotypical therapy archive 160.
  • This table represents the "cumulative experience" of the device with the particular patient in regards to therapy parameters, conditions under which therapy was applied as reflected by the pre-effectivity features, and the immediate outcome as reflected by post-effectivity features and success score for each stimulated event.
  • the z th row represents a particular therapy trial, while the/ h column represents a particular augmented feature coordinate.
  • the stereotypical therapy archive can look like Table 1 below: TABLE 1
  • the therapy profile [ X k ] is a row vector containing the therapy and channel configuration parameters.
  • such parameters may include current amplitude, pulse frequency, pulse width per phase, pulse train (burst) duration, limit on number of bursts per episode, electrode surface areas, stimulating electrode subset, bipolar electrode pairings and polarities, etc. Selecting a subset of electrodes from a relatively large number of channels via software partially addresses the electrode location question; physically varying the actual topographic locations post-surgery is clearly impracticable.
  • the therapy profile is held constant as X ⁇ for a number of trials, for example, as many as 800 or more therapy events can be collected in a few days, then it is switched to a different profile J -2 and held for some time and so on.
  • the various X k profiles are preferably quite distinct from each other, as if representing a random sampling of the large therapy parameter space. These initial profiles can be reasoned or alternatively obtained via pseudorandom number generators within known safety limits. A degree of randomness is important since the whole system will work in effect as a multistat! stochastic optimization algorithm, thus discouraging entrapment into local extrema of a "clinical utility function" as it learns therapy parameters that work best for the patient and the real-time conditions at hand.
  • parameters that might be used with other forms of therapy might include: (1) amount, concentration, rate, frequency (e.g. how often) and spatial zone of delivery, as well as the specific location of delivery of a drug, substance or medication delivered in response to changes in brain activity; (2) the intensity, frequency, spatial and dispersion of light application to a particular area; (3) the degree of temperature, magnetic or electric field strength delivered to a particular area, as well as its time course, frequency of delivery and spatial extent of delivery.
  • parameters to be measured in such instances include parameters that measure cellular as well as organ function (e.g. electrical), metabolic activity, perfusion/ blood flow, biochemical products of activity (e.g.
  • the pre- and post-effectivity features are stored as row vectors x w and y (( ' respectively on the z th therapy trial.
  • the calculation of each feature vector coordinate at a given instant of time requires observation of a "window" of the raw (neurologically-related) brain signals, opened to a certain extent spatially (multiple channels) and temporally (interval from a point in the past to the present time). For certain features computed recursively, this window is semi-infinite in extent but exponentially forgetful of the past.
  • This window "slides" across the time axis when a feature time series is desired, however, in the stereotypical therapy archive, the features x ⁇ and y ⁇ are associated with a single “snapshot" around a therapy event.
  • x (;) can be extracted from a 2-second window right-aligned to the therapy onset time
  • y w can be extracted from a 2-second window left-aligned to 2 seconds after the therapy onset time, as shown in FIG. 5.
  • the latter allows enough time for therapy artifact to subside, yet is early enough to take a valid reading of any acute post-therapy effect. Other methods of dealing with therapy artifact are known in the art.
  • the success score determined at 130 (FIG.
  • 4) may be a scalar S, which in the simple scheme is either 0 (unsuccessful) or 1 (successful seizure termination), but the skilled practitioner will recognize many other schemes are possible, including multilevel and continuous scoring, without departing from the spirit of the invention.
  • the observation period used in order to arrive at a success score need not strictly coincide with the post-effectivity feature observation window. For example, if success is defined as seizure termination within 5 seconds after the beginning of therapy, then the scorer (human or machine) will rely on the 3 -second window that is left-aligned with and completely overlapping the 2-second post-effectivity window ((d) plus 1 more second in FIG. 5).
  • the stereotypical therapy archive can be interpreted as a statistical sample of possibly dependent realizations drawn from a joint probability density function (PDF) p(X,x,y,S), where [X,x,y,S] is a row vector of random variables (all column names in the table). Integrations of this PDF, JJ- • • f p(X,x,y,S)dXdxdydS , can inform how frequent or how rare it is to observe the variables X, x, y, and S occurring together around the specific values (a row in the stereotypical therapy archive table).
  • PDF probability density function
  • this function tends to be stationary or cyclostationary, i.e., the input variables vary with time, but the PDF transformation itself does not vary, or does so only periodically with the circular clock variables (in which case augmenting the effectivity feature vector with such variables converts the cyclostationary PDF into a stationary one). For example, if an individual reports having seizures often on awakening (this pattern tends to persist over the course of years), then a circular histogram of the marginalized distribution of the linelength feature would show peaks at certain hours of the day, regardless of when the patient is examined, as long any changes in medication, etc. have been given time to settle.
  • the stereotypical therapy archive is preferably populated with events that have been captured without long device-off periods between them, and with device-on periods representing a relatively uniform sampling though time.
  • An important aspect of the invention is that several clinically relevant questions can be answered based on statistics of the stereotypical therapy archive.
  • This deemphasizes any role for how frequently the therapy profile X k was used, P(X X k ), which is initially manipulated, and instead emphasizes the probabilistic role of the remaining variables: effectivity features and therapy outcome, which are initially simply observed. Of clinical relevance is the calculation of probabilities conditioned on therapy profile.
  • the probability of success given a specific therapy is the fraction of Is in the score column for only the ⁇ -labeled block of rows to the total length of that block.
  • archive data may be stored that represents the effectivity features accumulated over time for a patient such that a probability of therapy efficacy can be computed based on the archive data.
  • the method described herein prescribes a probability of therapy success as a function of internal state and external input conditions present at the time of therapy delivery (as measured by the pre-effectivity features).
  • the notation xe x means that pre-effectivity vector x is currently visiting a particular region of the feature space (e.g., a crisp or fuzzy cell around x) denoted x , such as one with certain values of instantaneous phase, certain hours of the day, etc.
  • effectivity features such as instantaneous phase
  • Such fast exploratory features can be used to optimize the timing of therapy delivery by withholding stimulation until x enters a favorable region x known to increase the conditional success rate.
  • the circled region 190 represents a "critical phase range" discovered by the device in which stimulation has been more effective in the past.
  • the stimulator is triggered as shown at line 194).
  • therapy timing can be treated as yet another parameter to be tuned based on the effectivity features according to the automatic parameter selection techniques disclosed herein.
  • the probability function of interest is P(ye y Ix ⁇ X). This can be used to seek therapy profiles conditioned on x that specifically drive post- effectivity features y into a certain region y , such as one with decreased seizure spread, decreased energy, decreased complexity, decreased synchronization, etc., regardless of whether such outcome should be called "successful.”
  • an initial stereotypical therapy rulebase 170 is extracted from the stereotypical therapy archive 160 and periodically downloaded into the device.
  • the rulebase 170 is a much reduced look-up table that directly maps or translates each coarse-grained real-time condition x into its pre-optimized therapy profile X .
  • the therapy to choose at any given time is the one that maximizes probability of acute therapy success given the real-time conditions and the cumulative experience thus far:
  • this rule says "if the pre-effectivity feature vector x is currently in region x , select the therapy profile from among the K known so far that was most frequently associated with success whenever x was visiting that same region in the past.”
  • the stereotypical therapy rulebase is updated at 290 as described hereinafter. Automatic selection of therapy parameters can be made in real-time based on the effectivity features.
  • the number of rows is determined by the desired granularity and/or the number of partition regions discernible from the stereotypical therapy archive.
  • An efficient addressing scheme can be used in order to identify which of the coarse-grained regions x w an incoming feature vector x belongs to without the need for distance calculations.
  • the coordinates of x can be converted to binary and the most significant bits used as the address into the look-up table.
  • Such analog-to- digital converters can be implemented at minimal power cost in implantable devices using two spiking electronic neurons configured as a hybrid state machine See for example R.
  • a smooth interpolating function can be synthesized using multivariate kernel smoothing, splines, neural networks, or any other method of continuous function synthesis from a discrete set of data points. Then gradient calculations can be performed numerically or analytically.
  • This new profile is added to an ever-increasing reference set, or it can be used to replace the worst performer in a fixed-size reference set.
  • the therapy rulebase data may be adjusted based on changes associated with a multivariate function that represents effectivity observations from among a finite set of therapy parameter reference profiles.
  • FIG. 7 shows an implementation of this online autonomous mode/process.
  • pre-effectivity features x are continuously monitored using hybrid digital-analog circuitry and rolling buffers in a similar manner as practiced in currently available seizure detection devices.
  • function 220 looks up the location of input x in the x column of the on-board stereotypical therapy rulebase, reads the corresponding set of pre-optimized therapy parameters in the X ** column, and loads these parameters into the system for timed delivery. Because there is only memory transfer but no calculation involved, the function 220 occurs almost instantaneously. Any further delay between this time and actual therapy delivery depends on whether the fired rule contains microtiming features such as instantaneous phase.
  • post-effectivity features are followed up for a few seconds after therapy delivery to determine if seizure activity has been abolished.
  • the prediction implied by use of the stereotypical seizure onset discriminator is that full-blown seizure activity would be present at this time.
  • it is determined whether the therapy was a success. That is, if post-effectivity features are within thresholds indicative of successful termination, the whole event automatically receives success score S I at 250. On the other hand, if therapy failed to stop the seizure, the success score S is set to 0 at 260 and at 270 a flag is set indicating request for a new, improved therapy profile to be deployed the next time feature x visits region x .
  • the [X,x,y,S] vector associated to this event is used to update the stereotypical therapy rulebase at 290 stored in the on-board memory for future asynchronous uploading into the stereotypical therapy archive at 280. It should be noted that in this autonomous mode, storage of the raw electrograms is no longer necessary. Finally, the stereotypical therapy rulebase may be updated on the next device interrogation, with reinforcement for the known successful profiles, or with a new therapy profile computed from gradients if such request was made by the device.
  • the therapy rulebase data stored within the system 1000 that is attached to or implanted in the patient may be derived on-demand or periodically from a more comprehensive collection of stereotypical therapy data stored separately from the system 1000 in a device or system that is not implanted or attached in the patient is derived.
  • FIG. 8 illustrates an example of the system 1000 in which the biological sensor subsystem 1100 comprises signal transducers 300 that sense EEG signals and the therapy application subsystem 1300 comprises an IPG 400. That FIG. 8 deals with EEG signals only and a stimulation therapy is meant only by way of example. Other biological signals may be processed in a similar manner, and other therapy regimens may be applied as well.
  • the signal transducers 300 are provided that include electrodes and electronics to obtain digital EEG data.
  • a storage buffer 370 e.g., a rolling buffer, continually stores EEG data for a most recent duration of time, e.g., the last 2 minutes, analogous to a stripchart.
  • a pre-event EEG contained (from t ⁇ -2min. to t ⁇ ) in the rolling buffer 370 is copied into electrogram storage unit (e.g., memory) 380.
  • electrogram storage unit e.g., memory
  • the rolling buffer now contains post-event EEG.
  • a complete 4-minute electrogram from t ⁇ -2min. to t ⁇ +2min with respect to an EEG event is obtained.
  • the electrogram storage may have limited storage capacity, only a finite number of such electrograms that can be stored on-board the patient implanted or carried equipment. Therefore, the storage buffer 370 can be periodically interrogated for transfer of EEG data into an external EEG archive (50 shown in FIGs. 3 and 4) through a telemetry and programming interface device 500 as is known in the art.
  • stereotypical seizure onset features are extracted from the EEG data.
  • pre-effectivity features are extracted from the real-time EEG data.
  • a seizure detector 330 receives as input the seizure features triggers the above electrogram storage operation by of a timer 360. This is needed for all seizures rather than only a stereotypical subset in order to document clinical endpoints.
  • the stereotypical onset discriminator (SOD) 335 operates as a refinement immediately after seizure detection and triggers the firing of a pre-optimized set of therapy parameters that are loaded into the neurostimulator 400 "on-the-fly" and that are determined to be appropriate for the existing brain condition x(t), based on the continuously extracted pre-effectivity features.
  • an inference engine 340 matches the input x to the "closest” (not necessarily in the Euclidean sense) known condition in the stored stereotypical therapy rulebase 350 in order to formulate the "best" therapy parameters.
  • the SOD 330 asserted condition also triggers the neurostimulator 400 through a small delay shown at 390 to ensure the appropriate parameters have finished loading.
  • the signal processing blocks shown in FIG. 8, such as blocks 310, 320, 330, 335, 340, 350, 360, 370 and 390 may be implemented by one or more software or computer programs stored in computer readable medium and executed by a computer or microprocessor.
  • Signal sensing and therapy delivery to the brain may be made by any electrode wire structures heretofore known or hereinafter developed.
  • the wires/electrodes may be mesh-networked sensors and microstimulators that are operated wirelessly.
  • the system and method described herein may be deployed in various platforms.
  • the system and method described herein may be fully automated and software-driven, and embodied in an external-wearable device (e.g., clipped to the belt) and worn by a patient during the learning/training phases.
  • the patient wears the device after the appropriate IPG components, drug delivery components and related electrodes are implanted and the body-wearable device downloads data collected from the implanted device (e.g., IPG and related electrodes) processes and trains on the data, (or off-loads the data remotely for training on a separate computing apparatus), then uploads the trained software from the separating computing apparatus. After training/learning is complete, the patient need not wear the computing device any longer.
  • data collected from the implanted device e.g., IPG and related electrodes
  • the body-wearable device downloads data collected from the implanted device (e.g., IPG and related electrodes) processes and trains on the data, (or off-loads the data remotely for training on a separate computing apparatus), then uploads the trained software from the separating computing apparatus.
  • the patient need not wear the computing device any longer.
  • system and method of the present invention may be part of a closed-loop continuous control system. That is, the on-the-fly parameter loading operation to the IPG can be made so fast that the unit implanted in the patient may provide essentially continuous feedback control.
  • the implementation of a controller using a therapy rulebase as described herein is similar to fuzzy logic controllers and gain- scheduled controllers (where if the state x(t) of the system is in region A then use parameters A' to control; if in region B then use B', etc.).
  • One application is to analyze the effectivity features in order to identify sites in the patient where a therapy would be most effective based on at least one of: sites where earliest neurological disorder activity is detected, sites where high frequency activity occurs early in a neurological disorder event, synchrony with respect to all possible pairs of signals common to two or more adjacent or functionally connected regions, as measured by the appropriate sensors.
  • This concept also applies to detectable and reliable patterns of statistical correlation or neuronal activity, such as fast ripple, ripples, high frequency oscillations, single cell or multicellular activity, and abstract parameters that might measure this activity.
  • effectivity features are to adjust a sensitivity of a neurological disorder event detection process (e.g., seizure detector).
  • a neurological disorder event detection process e.g., seizure detector
  • an ideal neurological event detector would have maximum specificity for a given false negatives tolerance, such performance may be suboptimal in the closed loop of responsive therapy (the objective is to optimize therapy efficacy; this may or may not imply the optimal seizure detector).
  • the effectivity features disclosed herein provide an objective metric that can be used to guide the neurological event detector sensitivity setting. For example, clinical trial experience to date suggests certain patients benefit from hyper-sensitive seizure detectors. These settings trigger therapy more often, even if at the expense of numerous false positives.
  • a parameter of interest is known to occur in cellular networks generating epileptic seizures, such as fast ripples (e.g. characteristic 500 Hz oscillations)
  • the system could automatically alter its sensing parameters to enhance detection of these waveforms (by changing sliding windows for feature calculation, gain in the sensed signal, or altering the among particular features may be weighted in a classified-detection and classification system.
  • fast ripples e.g. characteristic 500 Hz oscillations
  • Still another use is to store a therapy archive comprising data representing the therapy rulebase data accumulated over time, and deriving from the archive markers to predict efficacy of a therapy in other patients.
  • the aggregate therapy rules learned numerous patients e.g., 100 or more
  • the registry can be data-mined for pre-effectivity features that more frequently resulted in successful therapy outcomes. This provides the bio-signal equivalent of "surrogate markers" for predicting therapy responders, and for matching optimal therapy profiles to new patients.
  • Another application of the therapy rulebase data is to generate parameters for a preventative treatment designed to interfere with generation of neurological disorder event in a patient, where the parameters for the preventative treatment may comprise at least one of a micro, macro, spatial or frequency characteristic.
  • successful therapeutic parameters can be employed as a responsive target after detection of conditions or changes in parameters that the device learns to associate with oncoming undesired events.

Abstract

La présente invention concerne un système et un procédé de neurothérapie automatique entièrement réceptif, qui permettent la reconnaissance et la sélection automatique de paramètres thérapeutiques efficaces. Ceci permet d'ajuster avec précision le réglage marche-arrêt de l'application de la thérapie, et d'améliorer ainsi l'efficacité sur toute la durée de vie du dispositif. Le système incorpore l'ajustement automatique de la nature, de la dose et du réglage de la neurostimulation des patients, et à la volée, sur une base événement par événement spécifique à un patient. Des caractéristiques 'd'efficacité pré- et post-thérapie' sont dérivées de signaux biologiques et de sous-systèmes de détection de capture. Par ailleurs, une 'base de règle thérapeutique' auto-adaptative est mise au point à partir des sous-systèmes de stockage/d'archivage, dans le but de rendre cohérentes ces caractéristiques 'd'efficacité' et, par la suite, de contrôler leurs trajectoires dans les sens d'une amélioration clinique.
PCT/US2008/055629 2007-03-02 2008-03-03 Sélection automatique de paramètres, et réglage thérapeutique permettant d'accroître l'efficacité des thérapies utilisant un dispositif neuronal réactif pour maladies et lésions cérébrales WO2008109508A2 (fr)

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