Detecting neuronal action potentials using a convolutive compound action potential model

Abstract

A hearing implant fitting system includes a physiological database containing physiological data characterizing auditory neural tissue response to electrical stimulation. A neural action potential (NAP) measurement system measures NAP signals from cochlear tissue responding to electrical stimulation signals delivered by one or more of the electrode contacts, including: deriving a compound discharge latency distribution (CDLD) of the cochlear tissue by deconvolving: (1) a tissue response measurement signal taken responsive to the delivered electrical stimulation signals, with (2) an elementary unit response signal representing voltage change at a measurement electrode contact due to the electrical stimulation, and then comparing the CDLD to physiological data from the physiological database to detect an NAP signal from the tissue response measurement signal. A fitting display provides to the fitting audiologist a visual display representing the CDLD and the NAP signal for fitting the electrode array to an implanted patient.

Claims

1. A hearing implant fitting system configured for use by a fitting audiologist to fit an electrode array implanted in a patient cochlea and having a plurality of electrode contacts for electrically stimulating adjacent neural tissue for perception as sound, the system comprising: a physiological database containing physiological data characterizing auditory neural tissue response to electrical stimulation; and a neural action potential (NAP) measurement system including a hardware-implemented processor executing software instructions for measuring NAP signals from cochlear tissue responding to electrical stimulation signals delivered by one or more of the electrode contacts, wherein measuring NAP signals includes: i. deriving a compound discharge latency distribution (CDLD) of the cochlear tissue by deconvolving: (a) a tissue response measurement signal taken responsive to the delivered electrical stimulation signals, with (b) an elementary unit response signal representing voltage change at a measurement electrode contact due to the electrical stimulation; ii. comparing the CDLD to physiological data from the physiological database to detect an NAP signal from the tissue response measurement signal; and a fitting display configured for providing to the fitting audiologist a visual display representing the CDLD and the NAP signal for fitting the electrode array to an implanted patient.

2. The system according to claim 1, wherein the physiological data is characterized by a plurality of Gaussian mixture models (GMMs).

3. The system according to claim 2, wherein the NAP measurement system is configured for comparing the CDLD to the GMM physiological data using a least mean square fitting.

4. The system according to claim 2, wherein the plurality of GMMs are two-component GMMs.

5. The system according to claim 2, wherein the plurality of GMMs include parameter distributions as a function of one or more of stimulation amplitude, inter-pulse interval during a recovery sequence, masker and stimulation level during a recovery sequence, stimulation pulse polarity, distance between a probe electrode and a masker electrode during a spread of excitation sequence, and medical device generation.

6. The system according to claim 2, wherein the plurality of GMMs include parameter distributions trained online by an expert to reflect a patient deviant parameter space.

7. The system according to claim 1, wherein the NAP measurement system is configured for comparing the CDLD to the physiological data based on using one or more of scale, latency and variation.

8. The system according to claim 1, wherein the NAP measurement system is configured for deconvolving by using a fast-Fourier transform algorithm.

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

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

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

(3) 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.

(4) FIG. 3 shows the functional steps in a method of detecting neural action potential (NAP) signals from an obtained measurement recording (R) according to one specific embodiment of the present invention.

(5) FIG. 4 shows examples of measurement recordings containing an NAP at higher stimulation levels.

(6) FIG. 5 shows an example of an elementary unit response.

(7) FIG. 6 shows a CDLD computed by deconvolving according to an embodiment of the present invention.

(8) FIG. 7 shows a fitted two-component GMM.

(9) FIG. 8 shows distributions of fitted parameters of two-component GMM for physiological NAP responses.

DETAILED DESCRIPTION

(10) 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 are directed to a signal processing system that deconvolves the tissue response measurement signal recording with a known elementary unit response to obtain a compound discharge latency distribution (CDLD). The CDLD is then examined to contain physiological properties which are assumed to have originated from NAPs such as an electrically-evoked compound action potential (eCAP) signal.

(11) An NAP signal technically is a compound signal that represents the sum of a large number of synchronously occurring voltage changes due to electrically excited nerve fibers. The inventors found, that usage of a convolution model (see, e.g., Goldstein, M. H.; Kiang, N. Y. S. Synchrony of neural activity in electric responses evoked by transient acoustic stimuli JASA, Vol. 30, pp. 107-114 (1958); incorporated herein by reference in its entirety) to describe the NAP response x(t) using the following equation is suitable:
x(t)=N.sub..sup.tP()U(t)dEq. (1)
where N represents the number of excited nerve fibers observable at the recording electrode, P(t) is the compound discharge latency distribution (CDLD) of the observable neural population, and U(t) is the voltage change at the electrode due to a single unit. Based on recordings in guinea pigs (see, e.g., Versnel, H.; Schoonhoven, R.; Prijs, V. F. Single-fibre and whole-nerve responses to clicks as a function of sound intensity in the guinea pig Hearing Research, Vol. 59, pp. 138-156 (1992); incorporated herein by reference in its entirety), the single unit response U(t) can be modeled by the following equations with for example U.sub.N=0.12e-6 V, .sub.N=0.12e-3 describing the negative part, and U.sub.P=0.045e-6 V, .sub.P=0.16e-3 describing the positive part, and t.sub.0=0.06e-3 s defines the cross point:

(12) $\begin{array}{cc}U\left(t\right)=\frac{U_N}{{}_N}\left(t-t_0\right)e^{\frac{1}{2}-\frac{{\left(t-t_0\right)}^2}{2{}_N^2}},t<t_0& \mathrm{Eq}.\left(2a\right)\\ U\left(t\right)=\frac{U_P}{{}_P}\left(t-t_0\right)e^{\frac{1}{2}-\frac{{\left(t-t_0\right)}^2}{2{}_P^2}},t{t}_0& \mathrm{Eq}.\left(2b\right)\end{array}$
The CDLD P(t) defines how many nerve fibers discharge as a function of post-stimulus time and the inventors found that it can be modeled by a two-component Gaussian mixture model (GMM) as denoted in the following equation 3 with, for example, .sub.1=0.75e-3 s, .sub.1=125e-6, .sub.2=1.50e-3 s and .sub.2=1000e-6 and scale factor 3/2.
P(t)=(.sub.1,.sub.1.sup.2)+3/2(.sub.2,.sub.2.sup.2)Eq. (3)
In a more general form the CDLD P(t) may be expressed by
P(t)=(1s)(.sub.1,.sub.1.sup.2)+s(.sub.2,.sub.2.sup.2)
Where .sub.1 and .sub.2 are the mean values, corresponding to the latency, and .sub.1 and .sub.2 the standard deviations of the first and second Gaussian component. The scale factor s describes the weighting of the two components to each other and completes the parameter-set. It is to be understood, that any other suitable GMM may be used as well.

(13) Based on the foregoing, embodiments of the present invention solve the inverse problem of Equation 1 for the tissue response measurement signal recording R to obtain the CDLD P(t), and analyze the resultant P(t) to recognize if an NAP signal is present. FIG. 2 shows various functional blocks 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. 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 10 card, a RIB II isolation box, and a communications cable between 10 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 with the implant electrodes and transmitted by wire and/or wirelessly via the external transmitter 203, the control interface 202 to the response measurement module 201. It is understood, that any other way of communication between implant and control interface 202 or measurement module 201 may be equally possible. For example a direct wireless transmission from the implant to the control interface 202 as is for example advantageous for total implantable cochlear implants. Response measurement module 201 compares the measurement signals to known physiological data from Physio Database 204 as described below to detect NAPs such as eCAPs within the measurement signals.

(14) FIG. 3 shows the functional steps in a method of detecting neural action potential (NAP) signals from neural tissue responding to electrical stimulation signals according to one specific embodiment of the present invention. First in step 301 the CDLD is derived by deconvolving the measurement R in response measurement module 201, then parameters are derived to characterize the CDLD in step 302. The derived parameters characterizing the CDLD are compared in step 303 using the Physio Database 204 with known parameters from physiological responses and if the recording R contains a CDLD with parameters within the physiological range a detected NAP is reported. FIG. 4 shows some examples of such measurement signal recordings R that contain an NAP at higher stimulation levels.

(15) The response measurement module 201 derives a compound discharge latency distribution (CDLD) of the neural tissue by deconvolving the measurement signal with an elementary unit response signal (See FIG. 5) representing voltage change at the recording electrode due to the electrical stimulation of a nerve fiber, step 301. For example, a fast-Fourier transform may be used for this.

(16) FIG. 6 shows a series of examples where the CDLD P(t) is computed by deconvolving the example measurement signals R from FIG. 4 with an elementary unit response U(t) as from FIG. 5. The example fitting display 205 of a CDLD shown in FIG. 6 also can usefully serve a visualization of the CDLD in a fitting software application for use by a fitting audiologist to allow the audiologist to easily see the characteristic shape of the response without having to delve into hard to understand values such as are often output from a complicated measurement and fitting algorithm. Such a fitting display 205 of a CDLD presents a nerve firing probability in an intuitive and helpful picture for audiologist.

(17) The response measurement module 201 compares the CDLD to known physiological data from the Physio Database 204 to recover an NAP signal from the tissue response measurement signal R, step 303. For example, the physiological data in the Physio Database 204 may specifically include Gaussian mixture models (GMMs) such as two-component GMMs that the response measurement module 201 may fit to the CDLD using a least mean square algorithm. FIG. 7 shows an example of parameters of one such two-component GMM fitted to the CDLD.

(18) When the derived parameters characterizing the CDLD are similar to examples stored in the Physio Database 204, the response measurement module 201 reports a detected NAP in the tissue response measurement signal, step 303. Some typical median values are shown in Table 1 and FIG. 8 shows some typical distributions of fitted parameters of two-component GMMs for physiological NAP responses that include scale factor, latency, and standard deviation.

(19) TABLE-US-00001 TABLE 1 Median values for physiological NAP responses Scale Standard Gaussian Factor Latency Deviation Component s 1. 0.32 0.47 ms 0.19 ms 2. 0.71 1.01 ms 0.37 ms

(20) In specific embodiments, the parameter distributions of the fitted two-component GMMs in the GMM database 204 may be a function of one or more NAP recording parameters such as: Stimulation amplitude Inter-pulse interval during a recovery sequence Masker and stimulation level during a recovery sequence Polarity of stimulation pulse Distance on electrode array between masker and probe during a spread of excitation sequence Medical device generation
And in some embodiments, the parameter distributions can be trained online by an expert to reflect a subject's deviant parameter space (like for speech recognition system that initially have a universal parameter distribution data which is then trained to local speaker with a training text).

(21) Arrangements such as those described above provide low computational complexity resolution of NAPs from tissue response measurement signals based on physiological a priori knowledge of auditory nerve tissue.

(22) 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.

(23) 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).

(24) 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.