Detecting neuronal action potentials using a sparse signal representation

Abstract

A system and method detect neuronal action potential signals from tissue responding to electrical stimulation signals. A sparse signal space model for a set of tissue response recordings has a signal space separable into a plurality of disjoint component manifolds including a neural action potential (NAP) component manifold corresponding to tissue response to electrical stimulation signals. A response measurement module is configured to: i. map a tissue response measurement signal into the sparse signal model space to obtain a corresponding sparse signal representation, ii. project the sparse signal representation onto the NAP component manifold to obtain a sparse NAP component representation, iii. when the sparse NAP component representation is greater than a minimum threshold value, report and recover a detected NAP signal in the tissue response measurement signal.

Claims

1. A method for detecting neuronal action potential signals from tissue responding to electrical stimulation signals, and providing stimulation to address hearing impairment, the method comprising: providing stimulation pulses, via electrodes of a cochlear implant system, to neural tissue and detecting a tissue response measurement signal; transmitting the tissue response measurement signal to a computer; using the computer to perform steps of: accessing a sparse signal space model for a set of tissue response recordings, the sparse signal space model having a model signal space separable into a plurality of disjoint component manifolds including: a neural action potential (NAP) component manifold corresponding to tissue response to electrical stimulation signals, a stimulation artifact component manifold corresponding to artifacts due to the electrical stimulation signals, a source artifact component manifold corresponding to artifacts due to sources other than the electrical stimulation signals, and a noise artifact component manifold; and mapping the tissue response measurement signal into the model signal space to obtain a corresponding sparse signal representation; projecting the sparse signal representation onto the NAP component manifold to obtain a sparse NAP component representation; and when the sparse NAP component representation is greater than a minimum threshold value, reporting a detected NAP signal in the tissue response measurement signal; determining an operating parameter for the cochlear implant system based on the detected NAP signal; transmitting the operating parameter to the cochlear implant system; and providing, by the cochlear implant system, stimulation pulses to the electrodes, the pulses based, at least in part, on the operating parameter.

2. The method according to claim 1, further comprising: reperforming projecting, and reporting for at least one other component manifold in the model signal space.

3. The method according to claim 2, wherein the at least one other component manifold includes the stimulation signal artifact component manifold and a stimulation artifact signal is detected.

4. The method according to claim 1, wherein the detected NAP signal is an electrically-evoked compound action potential (eCAP) signal.

5. The method according to claim 1, wherein the sparse signal space model is a MOD or K-SVD trained model.

6. The method according to claim 1, wherein the NAP component manifold is constrained by a NAP signal model.

7. The method according to claim 1, wherein the minimum threshold value is a fixed constant value.

8. The method according to claim 1, wherein the minimum threshold value is a variable function of one or more components in the tissue response measurement signal.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows anatomical structures of a human ear having a cochlear implant system.

(2) FIG. 2 shows various components in a system for measuring neural action potential (NAP) signals from tissue responding to electrical stimulation signals according to one specific embodiment of the present invention.

(3) FIG. 3 shows the functional steps in a method of measuring neural action potential (NAP) signals from tissue responding to electrical stimulation signals according to one specific embodiment of the present invention.

DETAILED DESCRIPTION

(4) Instead of using complex detection algorithms such as template matching or machine-learned expert systems such as decision tree classifiers to recognize possible NAPs directly in the tissue response measurement recording, embodiments of the present invention maps the recording into a sparse signal space using for example a MOD or K-SVD trained signal model to obtain a sparse signal representation which allows a robust and computationally inexpensive signal detection and classification of possible NAPs and signal artifacts within this signal space.

(5) FIG. 2 shows various functional blocks in a system for measuring neural action potential (NAP) signals from tissue responding to electrical stimulation signals and FIG. 3 shows the functional steps in a method of measuring neural action potential (NAP) signals from tissue responding to electrical stimulation signals according to embodiments of the present invention. Response measurement module 201 contains a combination of software and hardware for generating electrical stimulation pulses for the target neural tissue and recording and analyzing the NAPs. For example, the response measurement module 201 may be based on a Research Interface Box (RIB) II system manufactured at the University of Technology Innsbruck, Austria which may include a personal computer equipped with a National Instruments digital IO card, a RIB II isolation box, and a communications cable between IO card and RIB II box. The electrical stimulation pulses are transmitted from the response measurement module 201 through a control interface 202 to an external transmitter 203 which transmits them through the skin to implant electrodes to the target neural tissue. The NAP responses are recorded at the implant electrodes and transmitted via the external transmitter 203 through the control interface 202 to the response measurement module 201.

(6) Initially, a sparse signal space model S is trained for a set of tissue response recordings in a form r=a+b+c+d, where r∈R=.sup.N is an individual tissue response recording representing a signal mixture, a∈.sup.N is a neural action potential (NAP) component of r, b∈.sup.N is a stimulation artifact component of r, c∈.sup.N is an other source artifact component of r, d∈.sup.N is a noise component of r, where the sparse signal model S: .sup.N.fwdarw..sup.M such that a sparse signal representation r.sub.s=ƒ.sub.s(r) with min.sub.r.sub.s∥r.sub.s∥.sub.0, and a.sub.s=ƒ.sub.s(a), a.sub.s∈A=.sup.α⊂S, α<M, i. b.sub.s=ƒ.sub.s(b), b.sub.s∈B=.sup.β⊂S, β<M, ii. c.sub.s=ƒ.sub.s(c), c.sub.s∈C=.sup.γ⊂S, β<M,
with A∩B=Ø, A∩C=Ø, B∩C=Ø and α+β+γ<M. The minimum of a function with respect to a variable x is denoted as min.sub.x ƒ(x). The L0 norm, which corresponds to the number of non-zero elements, is denoted as ∥⋅∥.sub.0. That is, the sparse signal space S is separable into multiple disjoint component manifolds (A, B, C). Training of the sparse signal space model S only needs to be done once with a sufficiently large number of known tissue response recordings and for each of the component manifolds.

(7) The response measurement module 201 then accesses the sparse signal space model, step 301, and derives a sparse signal representation r.sub.s for the tissue response measurement signal r using the predefined sparse signal space model S, step 302. The response measurement module 201 then projects the sparse representation r.sub.s onto all predefined manifolds, step 303; for example, projecting the sparse representation r.sub.s onto the NAP component manifold A to obtain a sparse representation a.sub.s of a possible NAP, step 304. The response measurement module 201 then reports if a predefined signal was present in the tissue response measurement signal r when the signal energy of the sparse representation is greater than a minimum threshold value energy; e.g., a NAP component a is reported if the derived ∥a.sub.s∥>a.sub.thr, step 305.

(8) If the stimulation artifact signal b is desired, then the response measurement module 201 projects sparse representation r.sub.s into the stimulation artifact manifold B, and likewise for source artifact component signal c and the noise component d. This system allows a measurement analysis using just computationally inexpensive projections. That reduces the computational complexity considerably, and furthermore, operating within the sparse signal space is very efficient since many of the signal coefficients are zero. Furthermore this system mimics the signal processing of neurosensory systems that are optimized to perform in a robust manner.

(9) Once the projection into the predefined sparse signal space has been done, the needed energy to detect a component signal a, b, c or d can be calculated or looked up in a table. For example, a look up table may store the energy for the associated NAP signal of the NAP component manifold in the sparse signal space. If the energy level of the signal component is above some minimum threshold value, then the NAP signal has been recovered. This energy threshold may be a fixed level, or in some embodiments, it may be a variable function of one or more of the components in the tissue response measurement signal. For example, if the stimulation artifact b has a relatively high signal energy, that suggests that the reference electrode contact has high impedance and may need to be checked. It also suggests that the estimate of the NAP signal needs to be done very carefully, and so the energy threshold for the NAP signal may accordingly be increased.

(10) Embodiments of the invention may be implemented in part in any conventional computer programming language. For example, preferred embodiments may be implemented in a procedural programming language (e.g., “C”) or an object oriented programming language (e.g., “C++”, Python). Alternative embodiments of the invention may be implemented as pre-programmed hardware elements, other related components, or as a combination of hardware and software components.

(11) Embodiments also can be implemented in part as a computer program product for use with a computer system. Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (e.g., a diskette, CD-ROM, ROM, or fixed disk) or transmittable to a computer system, via a modem or other interface device, such as a communications adapter connected to a network over a medium. The medium may be either a tangible medium (e.g., optical or analog communications lines) or a medium implemented with wireless techniques (e.g., microwave, infrared or other transmission techniques). The series of computer instructions embodies all or part of the functionality previously described herein with respect to the system. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the network (e.g., the Internet or World Wide Web). Of course, some embodiments of the invention may be implemented as a combination of both software (e.g., a computer program product) and hardware. Still other embodiments of the invention are implemented as entirely hardware, or entirely software (e.g., a computer program product).

(12) Although various exemplary embodiments of the invention have been disclosed, it should be apparent to those skilled in the art that various changes and modifications can be made which will achieve some of the advantages of the invention without departing from the true scope of the invention.