Determination of neuronal action potential amplitude based on multidimensional differential geometry

Arrangements are described for determining a physiological characteristic of the auditory pathway (as whole or selected parts such as an inner ear). Electrical stimulation pulses are delivered to inner ear neural tissue and corresponding tissue response signals are developed by measuring over time response of the auditory pathway to each electrical stimulation pulse, with each tissue response signal forming a response curve including at least one physiological landmark such as a local maximum and a local minimum. A multi-dimensional polynomial is fit over the tissue response signals, and calculation starting points are defined based on prominent physiologic landmarks such as a local maximum and a local minimum for one selected tissue response signal. A line of minimum principal curvature of the multi-dimensional polynomial over the plurality of tissue response signals that intersect the calculation starting points is calculated to determine a physiological characteristic of the auditory pathway.

TECHNICAL FIELD

The present invention relates to detecting neuronal action potential signals from tissue responding to electrical stimulation signals, especially for hearing implant systems such as cochlear implant systems.

BACKGROUND ART

Most sounds are transmitted in a normal ear as shown inFIG. 1through the outer ear101to the tympanic membrane (eardrum)102, which moves the bones of the middle ear103(malleus, incus, and stapes) that vibrate the oval window and round window openings of the cochlea104. The cochlea104is a long narrow duct wound spirally about its axis for approximately two and a half turns. It includes an upper channel known as the scala vestibuli and a lower channel known as the scala tympani, which are connected by the cochlear duct. The cochlea104forms an upright spiraling cone with a center called the modiolus where the spiral ganglion cells of the acoustic nerve113reside. In response to received sounds transmitted by the middle ear103, the fluid-filled cochlea104functions as a transducer to generate electric pulses which are transmitted to the cochlear nerve113, and ultimately to the brain.

Hearing is impaired when there are problems in the ability to transduce external sounds into meaningful action potentials along the neural substrate of the cochlea104. To improve impaired hearing, auditory prostheses have been developed. For example, when the impairment is associated with the cochlea104, a cochlear implant with an implanted stimulation electrode can electrically stimulate auditory nerve tissue with small currents delivered by multiple electrode contacts distributed along the electrode. These electrodes may also be used for sensing neural tissue response signals, i.e. function as measurement electrodes.

In some cases, hearing impairment can be addressed by a cochlear implant (CI), a brainstem-, midbrain- or cortical implant that electrically stimulates auditory nerve tissue with small currents delivered by multiple electrode contacts distributed along an implant electrode. For cochlear implants, the electrode array is inserted into the cochlea. For brain-stem, midbrain and cortical implants, the electrode array is located in the auditory brainstem, midbrain or cortex, respectively.

FIG. 1shows some components of a typical cochlear implant system where an external microphone provides an audio signal input to an external signal processor111which implements one of various known signal processing schemes. For example, signal processing approaches that are well-known in the field of cochlear implants include continuous interleaved sampling (CIS) digital signal processing, channel specific sampling sequences (CSSS) digital signal processing (as described in U.S. Pat. No. 6,348,070, incorporated herein by reference), spectral peak (SPEAK) digital signal processing, fine structure processing (FSP) and compressed analog (CA) signal processing.

The processed signal is converted by the external signal processor111into a digital data format, such as a sequence of data frames, for transmission by an external coil107into a receiving stimulator processor108. Besides extracting the audio information, the receiver processor in the stimulator processor108may perform additional signal processing such as error correction, pulse formation, etc., and produces a stimulation pattern (based on the extracted audio information) that is sent through electrode lead109to an implanted electrode array110. Typically, the electrode array110includes multiple stimulation contacts112on its surface that provide selective electrical stimulation of the cochlea104.

To collect information about the electrode-nerve interface, a commonly used objective measurement is based on the measurement of Neural Action Potentials (NAPs) such as the electrically-evoked Compound Action Potential (eCAP), as described by Gantz et al., Intraoperative Measures of Electrically Evoked Auditory Nerve Compound Action Potentials, American Journal of Otology 15 (2):137-144 (1994), which is incorporated herein by reference. In this approach, the recording electrode is usually placed at the scala tympani of the inner ear. The overall response of the auditory nerve to an electrical stimulus is measured typically very close to the position of the nerve excitation. This neural response is caused by the super-position of single neural responses at the outside of the auditory nerve membranes.

FIG. 2shows an example of measuring eCAP amplitude based solely on time since stimulation for a single response signal recording. The response signal is characterized by the amplitude between the minimum voltage (this peak is called typically N1) and the maximum voltage (peak is called typically P2), the so-called local extrema. These extrema among others represent the most prominent physiological landmarks of the ECAP signal. The amplitude of the eCAP at the measurement position is in most cases between approximately 10μV and 1800 μV. One eCAP recording paradigm is a so-called “amplitude growth function,” as described by Brown et al., Electrically Evoked Whole Nerve Action Potentials In Ineraid Cochlear Implant Users: Responses To Different Stimulating Electrode Configurations And Comparison To Psychophysical Responses, Journal of Speech and Hearing Research, vol. 39:453-467 (Jun. 1996), which is incorporated herein by reference. This function is the relation between the amplitude of the stimulation pulse and the peak-to-peak voltage of the eCAP.

In the past, relatively simple algorithms were used to determine the latencies of the extrema that represent physiological landmarks; for example, N1or P2from single recordings, which often produced physiologically unreasonable values that required manual correction of the determined latencies. Current state of the art methods are based on records of single pulses using only the time since stimulation as the basic factor for fitting functions providing the minima and maxima of the recorded signal. Standard sequences such as amplitude growth functions (AGF), recovery functions (RF) and spread of excitation functions (SoE) are especially affected by the lack of physiologic properties within the model, showing high variation in latencies of extrema or not detecting the extrema for single measurements due to signal artifacts. Artifacts, as understood in this context, are signal components in the recording not arising from physiologic effects that cannot be reduced by averaging multiple recordings.

Sophisticated algorithms are used to reduce the influence of signal artifacts from various sources; for example, alternating stimulation (Eisen M D, Franck K H: “Electrically Evoked Compound Action Potential Amplitude Growth Functions and HiResolution Programming Levels in Pediatric CII Implant Subjects.” Ear & Hearing 2004, 25(6):528-538; incorporated herein by reference in its entirety), masker probe (Brown C, Abbas P, Gantz B: “Electrically evoked whole-nerve action potentials: data from human cochlear implant users.” The Journal of the Acoustical Society of America 1990, 88(3):1385-1391; Miller C A, Abbas P J, Brown C J: An improved method of reducing stimulus artifact in the electrically evoked whole-nerve potential. Ear & Hearing 2000, 21(4):280-290; both incorporated herein by reference in their entirety), tri-phasic stimulation (Zimmerling M: “Messung des elektrisch evozierten Summenaktionspotentials des Hörnervs bei Patienten mit einem Cochlea-Implantat.” In PhD thesis Universität Innsbruck, Institut für Angewandte Physik; 1999; Schoesser H, Zierhofer C, Hochmair E S. Measuring electrically evoked compound action potentials using triphasic pulses for the reduction of the residual stimulation artifact. In: Conference on implantable auditory prostheses; 2001; both of which are incorporated herein by reference in their entirety), scaled template (Miller C A, Abbas P J, Rubinstein J T, Robinson B, Matsuoka A, Woodworth G: Electrically evoked compound action potentials of guinea pig and cat: responses to monopolar, monophasic stimulation. Hearing Research 1998, 119(1-2):142-154; incorporated herein by reference in its entirety), or amplitude template (Brown, C. J.; Hughes, M. L.; Luk, B.; Abbas, P. J.; Wolaver, A. and Gervais, J. “The relationship between EAP and EABR thresholds and levels used to program the nucleus 24 speech processor: data from adults.” Ear Hear, 21(2), pages 151-163, 2000; incorporated herein by reference in its entirety).

Even after applying various artifact reducing methods, the state-of-the art determination of extrema is not very robust (consistent for the whole sequence). The high failure rate and inaccuracy due to inaccurately detected extrema (and consequently eCAP-amplitudes) results in the use of other approaches to determine basic factors which are of interest for implant fitting such as the eCAP-threshold (SmartNRI as used by Advanced Bionics; Arnold, L. & Boyle, P. “SmartNRI: algorithm and mathematical basis.” Proceedings of 8th EFAS Congress/10th Congress of the German Society of Audiology, 2007; (AutoNRT™ as used by Cochlear Ltd.; Botros, A.; van Dijk, B. & Killian, M. “AutoNRT™: An automated system that measures eCAP thresholds with the Nucleus(R) Freedom(tm) cochlear implant via machine intelligence” Artificial Intelligence in Medicine, 2007, 40, 15-28; which are incorporated herein by reference in their entirety).

Despite the weak performance of the fitting functions based only on time since stimulus to correctly determine the response signal amplitude, they still are used by filtering the basic recorded signal and subtracting a disturbing artifact. Standard evaluation procedures are highly affected by the resulting highly variable amplitude values and so need to be manually evaluated, or else specialized procedures can be used to determine subsequent values.

SUMMARY

Embodiments of the present invention are directed to arrangements for determining a physiological characteristic of auditory path (in whole or in part such as the inner ear). Electrical stimulation pulses are delivered to neural tissue of the auditory pathway (either as a whole or selected parts) and corresponding tissue response signals are developed by measuring over time response of the inner ear neural tissue to each electrical stimulation pulse, with each tissue response signal forming a response curve. A multi-dimensional polynomial is fit over the tissue response signals, and calculation starting points are defined for one selected tissue response signal. From the calculation starting points, a line of minimum principal curvature of the multi-dimensional polynomial over the plurality of tissue response signals that intersect the calculation starting points is calculated to determine a physiological characteristic of the inner ear.

The physiological characteristic may include an amplitude growth function where the multi-dimensional polynomial includes a post-stimulus time dimension and a stimulus intensity dimension. The physiological characteristic may include a tissue recovery function where the multi-dimensional polynomial includes a post-stimulus time dimension and an inter-stimulation pulse interval dimension. The physiological characteristic may include a spread of excitation function where the multi-dimensional polynomial includes a spatial distance dimension and an inter-stimulation pulse interval dimension. In specific embodiments, the multidimensional characteristic may include functions where the multi-dimensional polynomial includes different stimulation electrodes, different recording electrodes, different inter-phase gaps, various pulse shapes (e.g. biphasic, triphasic, etc.), different phase durations, various length of pulse trains (with or without amplitude modulation), measurements from different recording sessions over time and/or various masker-probe paradigms with or without changing ratios of masker/probe amplitudes. A skilled person can extend this list to further parameters that result in a continuous change of the physiologic response.

The line of minimum principal curvature may be calculated iteratively. Some embodiments may also output the closeness of the multi-dimensional fit as a function of the carrier of the measurements. The multi-dimensional polynomial may have a fixed degree or it may be a variable degree polynomial. The tissue response signals may specifically include electrically evoked compound action potential (eCAP) signals.

DETAILED DESCRIPTION

Embodiments of the present invention are based on a fitting of a multi-dimensional polynomial over multiple measurement tissue response signals. For example, besides time after delivery of the electrical stimulation pulse, the multi-dimensional polynomial fitting function also may reflect stimulation intensity (amplitude growth function, AGF) and/or time difference of a masking pulse (recovery functions, RF).FIG. 3shows an example of using multiple response signal recordings to measure eCAP amplitude as a function of both time since stimulus and stimulation intensity. It can be seen that the eCAP amplitude increases with stimulus intensity. In some specific embodiments, other additional factors can be incorporated into the fitting function (e.g. pairs of recording electrodes) by adding and/or replacing a corresponding dimension. Based on such multi-dimensional fitting function, the analysis of the resulting principal curvature yields a robust determination of the physiological landmarks of the signal such as extrema, and thereby the neuronal action potentials that are present within sequences of tissue response signals.

FIG. 4shows various components in a system for measuring neural action potential signals andFIG. 5shows the functional steps in a method of measuring neural action potential signals from tissue responding to electrical stimulation signals according to embodiments of the present invention. A tissue stimulation module401generates electrical stimulation pulses that a control interface module403sends to a cochlear implant404, which delivers the stimulation pulses (e.g., bi-phasic or tri-phasic stimulation pulses) to neural tissue in the auditory pathway (in whole or in selected part such as the inner ear, step501. In specific embodiments, the intensity of the electrical stimulation pulses may be varied; for example, increased or decreased for each measurement, and similarly the time between each electrical stimulation pulse may be either some predefined set interval or it may be varied for each measurement. In response to the delivered stimulation pulses, corresponding tissue response signals are developed by measuring the response over time of the auditory pathway neural tissue to each electrical stimulation pulse, step502. This is performed by sensing electrodes (e.g., cochlear implant electrode contacts) adjacent to the neural tissue. Each tissue response signal that forms a response curve including at least one local maximum and at least one local minimum (seeFIG. 2) is selected. These are sensed and may be averaged and delivered via the control interface module403to a response calculation module402.

The response calculation module402fits a multi-dimensional polynomial over the tissue response signals, step503, and defines calculation starting points for the physiological landmarks—for extrema they are based on a local maximum and a local minimum for one selected tissue response signal, step504. In a given set of multiple tissue response signals there may be some individual recordings that do not have a well-defined physiological landmarks such as local maxima and/or minima. Clearly such signals are not suitable to be selected for use as calculation starting points, and in specific embodiments, such outlier signals may or may not be included in the subsequent calculations. A given tissue response signal with the most prominent landmark e.g. lowest minimum and/or highest maximum may be selected as the basis for the calculation starting points. Or a given tissue response signal with the greatest difference between the minimum N1and maximum P2may be selected. Or tissue response signals with the strongest stimulation impulse (e.g., for amplitude growth function) or maximal time difference between a masker pulse and a probe pulse (recovery function).

From the calculation starting points, the response calculation module402iteratively calculates a line of minimum principal curvature of the multi-dimensional polynomial over the plurality of tissue response signals to determine a physiological characteristic of the inner ear, step505. For example, the physiological characteristic may include an amplitude growth function where the multi-dimensional polynomial includes a post-stimulus time dimension and a stimulus intensity dimension, a tissue recovery function where the multi-dimensional polynomial includes a post-stimulus time dimension and an inter-stimulation pulse interval dimension, and/or an spread of excitation function where the multi-dimensional polynomial includes a spatial distance dimension and an inter-stimulation pulse interval dimension. Optionally, once the line of minimum principle curvature has been calculated, a specific embodiment may further apply an energy function, step506, as described in further detail below.

The various fitting factors such as time since stimulation [ms], the stimulation intensity [μA], the time between first and second stimulus [ms], spatial distance between stimulation and recording electrodes, or any factor affecting a continuous change of the eCAP signal can be regarded as x1, x2, . . . xnrespectively. The measured tissue response signal [μV] is denoted as y, where x1, x2, . . . y ε. The fitting function is then modeled as a multi-dimensional polynomial where n εdenotes the number of modeled factors, diεis the degree of the polynomial along dimension i, and a(i1, i2, . . . , in)εare the corresponding weighting parameters:

Polynomials function evaluations can be calculated quickly, and partial derivations (and other further mathematical processes) can be calculated analytically. The number of the different weighting parameters a(i1, i2, . . . , in)εdepends on the specific degree of the polynomial d1, . . . , dnε. The multi-dimensional polynomial may have a fixed degree or it may be a variable degree polynomial. And the degree may be different for different dimensions. For example, the degree in the x1direction may be fixed at 6 while the degree in the x2direction may be fixed at 3. The degree may be fixed in one or more dimensions and variable in the one or more other dimensions. Fitting functions based only on time after stimulus (x1) are already known, so the degree for this influencing factor can be fixed as used in standard methods within a typical range: 10<d1<20. The degree of the other chosen influencing factors d2, . . . , dn, can be kept minimal and can be derived by successively increasing the degree [dj] for single factors and if the additional weighting parameters are significantly different from zero, that weighting parameter degree can be increased further. With a fixed set of weighting parameters and with each parameter appearing only linearly, the weighting parameters can be estimated by fast analytical linear regression models based on Vandermonde matrices. The initial estimate of the polynomial degree may start at a given fixed value such as 3, and the degree can then be increased and the fitting repeated with each such increase. If additional weighting parameters of the polynomial are close to zero, then no further degree increase may be needed. Increasing the degree of the polynomial may depend on a stability index along the considered dimension, which can be obtained as a by-product from the fitting. Partial derivations can be analytically given and deduced values can be determined based on analytical expressions.

For artifact-free tissue response signals, the extrema as most prominent physiological landmarks of the eCAP response due to the time after stimulus can be easily found using the first partial derivative of the amplitude function ∂/∂x1P(x1, x2, . . . , xn). If there are artifacts for individual tissue response signals, the multi-dimensional fit would inherently enable interpolation of the tissue response signals. Even when the signal extrema are completely hidden by the artifacts, the inner geometry of a multi-dimensional fit can be used to determine the extrema.

For example, as shown inFIG. 6, for given sequences of stimuli, the amplitude function can be projected in a three dimensional space (by fixing additional factors) and thus be considered as a surface embedded in3. For such a surface, the direction of the principal curvatures can be calculated analytically from P(x1, x2, . . . , xn) for single points (with fast computation times), and thus for given calculation starting points (e.g., for AGFs, the greatest stimulation intensity minimum N1of a single measurement) and small steps along one dimension (e.g. stimulation intensity [cu] for AGFs), the principal curvatures are given by iterative formulas. Because of the minimal curvature, the calculated direction yields to a line along the curve where the remaining minima N1or maxima P2are located for other stimulus intensities. Since the principal curvatures are within planes through the normal vector at a single point, this determination of extrema is much more robust for the case where linear signal artifacts cannot be completely filtered (as is typically the case for stimulation artifacts). In the small picture in the upper right corner ofFIG. 6, the eCAP answer is shown modeled as function depending on time since stimulus and stimulation intensity. In the main panel ofFIG. 6, a sketch of the mathematical definition of extrema determination is outlined: n denotes the normal vector, pc1respectively pc2the minimal and maximal principal curvatures. A change in amplitude depending on a change of the influencing factors can be described in a single point (here in N1at stimulation intensity1200[cu]) by the first partial derivation of the amplitude function, or alternatively by regarding the principal curvatures. The dotted line is showing the intercept of the amplitude function with a plane parallel to the tangent plane, reflecting roughly the constraints between curvature (“second partial derivation”) and change in amplitude. The two curves called minimal and maximal “principal curvatures” are intersect lines between planes through the normal vector and the amplitude function where the curvature of the resulting curves is at the minimal respectively maximal possible value.

So a specific algorithm for determining a curve where the extrema are located is given by a principal curvature which can be calculated as follows. First define a set of calculation starting points (E.g. extrema due to the first partial derivative of the amplitude function ∂/∂x1P(x1, x2, . . . , xd)). Beginning with the defined calculation starting point, make a step due to the direction of the minimal principal curvature until the entire range of the second factor of interest (for AGFs, stimulation intensity, for RFs, time between masker and probe pulse) is processed. This may be done for one selected calculation starting point or repeated for each calculation starting point and a curve or a set of curves is derived for (x1x2P(x1, x2, . . . , xn)) with x2, . . . , xnεfixed and a<x2<b; a, b, x2εand corresponding x1ε. In the latter case out of the resulting set of defined curves, the optimal curves for physiological landmarks such as N1and P2are selected.

Once the line of minimal principal curvature has been determined for each calculation starting point, some embodiments may further apply an energy function to the resulting set of defined curves. In this context, the higher the energy of one of the individual curves in the set of curves, the more likely it is that this curve is finally selected. The energy may further indicate the quality of how well the selected tissue response signal is representative for the physiologic characteristic. For example, if the energy is above a certain threshold, the tissue response signal is considered of good quality. The threshold may be fixed, for example heuristically determined, or may depend on response amplitude and/or signal-to-noise ratio and/or a statistical measure of the fitting. The energy function may include one or more components of stimulation pulse intensity (εint), measurement point response amplitude (εampl), difference to physiological landmarks such as extrema values (εext) and/or difference to average or mean values (εvals). Any of these components may be normalized. The energy function may for example be derived by multiplication of the one or more derived components.

ɛcurve=∑j∈{x2}⁢ɛvals,j·ɛext,j·ɛint,j·ɛampl,j
For an entire set of calculation starting points, the tissue response signal may be selected as representative of the physiologic characteristic based on an energy function. The selected tissue response signal may the one with maximum energy. In a further embodiment the tissue response signal may be optimized by means of a least mean square (LMS) fit over the resulting set of defined curves. In such an LMS fit, the standard deviations associated with each tissue response signal may vary; for example, as a function of the difference between the minimum and the maximum where a large difference indicates a reliable measurement with little measurement error (small standard deviation).

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.