U.S. patent application number 14/576804 was filed with the patent office on 2015-06-25 for detecting neuronal action potentials using a convolutive compound action potential model.
The applicant listed for this patent is MED-EL Elektromedizinische Geraete GmbH. Invention is credited to Angelika Dierker, Konrad Schwarz, Philipp Spitzer, Stefan Strahl.
Application Number | 20150173637 14/576804 |
Document ID | / |
Family ID | 53398777 |
Filed Date | 2015-06-25 |
United States Patent
Application |
20150173637 |
Kind Code |
A1 |
Strahl; Stefan ; et
al. |
June 25, 2015 |
Detecting Neuronal Action Potentials Using a Convolutive Compound
Action Potential Model
Abstract
A system and method detect neuronal action potential signals
from tissue responding to electrical stimulation signals. A
compound discharge latency distribution (CDLD) of the neural tissue
is derived by deconvolving a tissue response measurement signal
taken responsive to electrical stimulation of the neural tissue by
a stimulation electrode, with an elementary unit response signal
representing voltage change at a measurement electrode due to the
electrical stimulation. The CDLD is compared to known physiological
data to detect an NAP signal from the tissue response measurement
signal.
Inventors: |
Strahl; Stefan; (Innsbruck,
AT) ; Schwarz; Konrad; (Innsbruck, AT) ;
Dierker; Angelika; (Innsbruck, AT) ; Spitzer;
Philipp; (Innsbruck, AT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MED-EL Elektromedizinische Geraete GmbH |
Innsbruck |
|
AT |
|
|
Family ID: |
53398777 |
Appl. No.: |
14/576804 |
Filed: |
December 19, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61918915 |
Dec 20, 2013 |
|
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Current U.S.
Class: |
600/554 |
Current CPC
Class: |
A61B 5/0031 20130101;
A61B 5/04001 20130101; A61B 5/6817 20130101; A61N 1/0541 20130101;
A61N 1/36039 20170801; A61B 5/125 20130101; G06N 7/005 20130101;
A61B 5/7257 20130101; A61B 5/4041 20130101 |
International
Class: |
A61B 5/04 20060101
A61B005/04; G06N 3/063 20060101 G06N003/063; A61B 5/00 20060101
A61B005/00 |
Claims
1. A system for detecting a neuronal action potential (NAP) signal
from electrically stimulated neural tissue, the system comprising:
a physiological database containing physiological data
characterizing neural tissue response to electrical stimulation;
and a response measurement module configured to: i. derive a
compound discharge latency distribution (CDLD) of the neural tissue
by deconvolving: (a) a tissue response measurement signal taken
responsive to electrical stimulation of the neural tissue by a
stimulation electrode, with (b) an elementary unit response signal
representing voltage change at a measurement electrode due to the
electrical stimulation; ii. compare the CDLD to physiological data
from the physiological database to detect an NAP signal from the
tissue response measurement signal;
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 response
measurement module is configured to compare 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, distant 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 response measurement
module is configured to use one or more of scale, latency and
variation to compare the CDLD to the physiological data.
8. The system according to claim 1, wherein the response
measurement module is configured for deconvolving 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.
10. A method for detecting a neuronal action potential (NAP) signal
from electrically stimulated neural tissue, the method comprising:
deriving a compound discharge latency distribution (CDLD) of the
neural tissue by deconvolving: i. a tissue response measurement
signal taken responsive to electrical stimulation of the neural
tissue by a stimulation electrode, with ii. an elementary unit
response signal representing voltage change at a measurement
electrode due to the electrical stimulation; comparing the CDLD to
known physiological data to detect an NAP signal from the tissue
response measurement signal.
11. The method according to claim 10, wherein the known
physiological data is characterized by a plurality of Gaussian
mixture models (GMMs).
12. The method according to claim 10, wherein comparing the CDLD to
the GMM physiological data uses a least mean square fitting.
13. The method according to claim 10, wherein the plurality of GMMs
are two-component GMMs.
14. The method according to claim 10, wherein the plurality of GMMs
includes 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, distant between a probe electrode and a
masker electrode during a spread of excitation sequence, and
medical device generation.
15. The method according to claim 10, wherein the plurality of GMMs
includes parameter distributions trained online by an expert to
reflect a patient deviant parameter space.
16. The method according to claim 10, wherein comparing the CDLD to
the GMM physiological data includes comparing one or more of scale,
latency and variation.
17. The method according to claim 10, wherein the deconvolving uses
a fast-Fourier transform algorithm.
18. The method according to claim 10, wherein the NAP signal is an
electrically-evoked compound action potential (eCAP) signal.
Description
[0001] This application claims priority from U.S. Provisional
Patent Application 61/918,915, filed Dec. 20, 2013, which is
incorporated herein by reference.
TECHNICAL FIELD
[0002] 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
[0003] Most sounds are transmitted in a normal ear as shown in FIG.
1 through the outer ear 101 to the tympanic membrane (eardrum) 102,
which moves the bones of the middle ear 103 (malleus, incus, and
stapes) that vibrate the oval window and round window openings of
the cochlea 104. The cochlea 104 is 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 cochlea 104 forms an upright spiraling cone with
a center called the modiolus where the spiral ganglion cells of the
acoustic nerve 113 reside. In response to received sounds
transmitted by the middle ear 103, the fluid-filled cochlea 104
functions as a transducer to generate electric pulses which are
transmitted to the cochlear nerve 113, and ultimately to the
brain.
[0004] Hearing is impaired when there are problems in the ability
to transduce external sounds into meaningful action potentials
along the neural substrate of the cochlea 104. To improve impaired
hearing, auditory prostheses have been developed. For example, when
the impairment is associated with the cochlea 104, 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.
[0005] 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.
[0006] FIG. 1 shows some components of a typical cochlear implant
system where an external microphone provides an audio signal input
to an external signal processor 111 which 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.
[0007] The processed signal is converted by the external signal
processor 111 into a digital data format, such as a sequence of
data frames, for transmission by an external coil 107 into a
receiving stimulator processor 108. Besides extracting the audio
information, the receiver processor in the stimulator processor 108
may 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
lead 109 to an implanted electrode array 110. Typically, the
electrode array 110 includes multiple stimulation contacts 112 on
its surface that provide selective electrical stimulation of the
cochlea 104.
[0008] 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. The response 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 amplitude of the eCAP at the measurement
position is typically between 10 .mu.V and 1800 .mu.V. 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 (June 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.
Another clinically used recording paradigm is the so called
"recovery function" in which stimulation is achieved with two
pulses with varying interpulse intervals. The recovery function as
the relation of the amplitude of the second eCAP and the interpulse
interval allows conclusions to be drawn about the refractory
properties and particular properties concerning the time resolution
of the auditory nerve.
[0009] Detecting NAPs such as eCAPs is based on an analysis of an
obtained measurement recording (R) which can be understood as a
signal mixture containing the desired NAPs (A), artifacts due to
the stimulation (B) and other sources (C) and noise (D). The word
"artifact", as used in this document refers to all signal
components that are not caused by the eCAP response (except noise)
and are usually unwanted and subject of removal. A linear model of
this signal mixture is:
R=A+B+C+D
[0010] State-of-the-art NAP measurement systems apply special
recording sequences to reduce the unwanted artifacts and the noise
present during the measurement. The stimulation artifact (B) is
partially removed from the recording (R) by different measurement
paradigms such as "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; which is
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 of which are incorporated
herein by reference in their entireties), "tri-phasic stimulation"
(Zimmerling M: "Messung des elektrisch evozierten
Summenaktionspotentials des Hornervs bei Patienten mit einem
Cochlea-Implantat." In PhD thesis Universitat Innsbruck, Institut
fur 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
artefact," In: Conference on implantable auditory prostheses; 2001;
both of which are incorporated herein by reference in their
entireties), and "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; which is incorporated herein by reference in its
entirety). Artifacts due to other sources (C) are partially removed
by a zero amplitude template (Brown et al. 2000). The noise (D) is
reduced by repeated measurements, averaging over the repeated
recordings reduces the noise level by N for N repetitions.
[0011] These special recording sequences result in a processed
recording (R') with a reduced noise floor (D') and remaining
artifacts (B' and C) which in most cases are reduced in amplitude.
Some recording sequences also result in an altered NAP response
(A'), for example the "masker probe" paradigm (Westen, A. A.;
Dekker, D. M. T.; Briaire, J. J. & Frijns, J. H. M. "Stimulus
level effects on neural excitation and eCAP amplitude." Hear Res,
2011, 280, 166-176; which is incorporated herein by reference in
its entirety).
[0012] To automatically detect a NAP response in the resulting
recording (R') one commonly used technique is known as template
matching (SmartNRT 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; which is incorporated herein by reference in its
entirety). First an additional denoising of the recording (R') is
performed by calculating correlations with basis functions
predefined by a principal component analysis and performing
weighted summation, resulting in a recording (R'') with reduced
noise (see U.S. Pat. No. 7,447,549; which is incorporated herein by
reference in its entirety). Then an artifact model
(B.sub.Model+C.sub.Model) representing the sum of two decaying
exponentials is fitted to this post-processed recording (R'') and
with a strength of response metric
(SOR=(R''-B.sub.Model-C.sub.Model)/noise) a threshold is determined
to detect a possible NAP (A) (U.S. Pat. No. 7,818,052; which is
incorporated herein by reference in its entirety).
[0013] Another approach to automatically detect a NAP response in
the resulting recording (R') is known as expert system (AutoNRT.TM.
as used by Cochlear Ltd.; Botros, A.; van Dijk, B. & Killian,
M. "AutoNRT.TM.: An automated system that measures ECAP thresholds
with the Nucleus.RTM. Freedom.TM. cochlear implant via machine
intelligence" Artificial Intelligence in Medicine, 2007, 40, 15-28;
which is incorporated herein by reference in its entirety). The
expert system used is a combination of a template matching and a
decision tree classifier (U.S. Patent Publication US 20080319508
A1; which is incorporated herein by reference in its entirety). The
template matching classifier computes the correlation with a NAP
(A) template and a NAP plus stimulation artifact (A+B) template.
The decision tree uses the following six parameters: [0014] N1-P1
amplitude for NAP typically latencies [0015] noise level [0016]
ratio N1-P1 amplitude to noise level [0017] correlation with NAP
(A) template [0018] correlation with NAP plus stimulation artifact
(A+B) template [0019] correlation between this measurement (R) and
a previous measurement at a lower stimulation amplitude. Two
different decision tree classifiers were learned with a C5.0
decision tree algorithm. For the case where no NAP (A) was detected
at lower stimulation levels, the stimulation level was increased
and a decision tree with a low false positive rate was used to
determine the presence of a NAP (A). For the case where a NAP (A)
was detected, the stimulation level was reduced and a decision tree
with a low overall error rate was used to evaluate the presence of
a NAP (A).
SUMMARY
[0020] Embodiments of the present invention are directed to a
system and method to detect neuronal action potential signals from
tissue responding to electrical stimulation signals. A compound
discharge latency distribution (CDLD) of the neural tissue is
derived by deconvolving a tissue response measurement signal taken
responsive to electrical stimulation of the neural tissue by a
stimulation electrode, with an elementary unit response signal
representing voltage change at the stimulation electrode due to an
activation of a single nerve fiber due to the electrical
stimulation. The CDLD is compared to known physiological data to
detect an NAP signal within the tissue response measurement
signal.
[0021] The physiological data may be characterized by a plurality
of Gaussian Mixture Models (GMM) such as two-component GMM. The
derivation of the GMMs parameters may use a least mean square
fitting. And the GMMs may 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, spatial
distance between probe and masker electrodes during a spread of
excitation sequence, and medical device generation, and the GMMs
may include parameter distributions trained online by an expert to
reflect a patient deviant parameter space. Comparing the CDLD to
known physiological data may include comparing one or more of
scale, latency and variation. A fast-Fourier transform algorithm
may be used for the deconvolution. The NAP signal may be an
electrically-evoked compound action potential (eCAP) signal.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] 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.
[0023] FIG. 1 shows anatomical structures of a human ear having a
cochlear implant system.
[0024] 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.
[0025] 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.
[0026] FIG. 4 shows examples of measurement recordings containing
an NAP at higher stimulation levels.
[0027] FIG. 5 shows an example of an elementary unit response.
[0028] FIG. 6 shows a CDLD computed by deconvolving according to an
embodiment of the present invention.
[0029] FIG. 7 shows a fitted two-component GMM.
[0030] FIG. 8 shows distributions of fitted parameters of
two-component GMM for physiological NAP responses.
DETAILED DESCRIPTION
[0031] 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.
[0032] 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.intg..sub.-.infin..sup.tP(.tau.)U(t-.tau.)d.tau. Eq. (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, .sigma..sub.N=0.12e-3 describing the negative part, and
U.sub.P=0.045e-6 V, .sigma..sub.P=0.16e-3 describing the positive
part, and t.sub.0=-0.06e-3 s defines the cross point:
U ( t ) = U N .sigma. N ( t - t 0 ) 1 2 - ( t - t 0 ) 2 2 .sigma. N
2 , t < t 0 Eq . ( 2 a ) U ( t ) = U P .sigma. P ( t - t 0 ) 1 2
- ( t - t 0 ) 2 2 .sigma. P 2 , t .gtoreq. t 0 Eq . ( 2 b )
##EQU00001##
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, .mu..sub.1=0.75e-3
s, .sigma..sub.1=125e-6, .mu..sub.2=1.50e-3 s and
.sigma..sub.2=1000e-6 and scale factor 3/2.
P(t)=(.mu..sub.1,.sigma..sub.1.sup.2)+3/2(.mu..sub.2,.sigma..sub.2.sup.2-
) Eq. (3)
In a more general form the CDLD P(t) may be expressed by
P(t)=(1-s).times.(.mu..sub.1,.sigma..sub.1.sup.2)+s.times.(.mu..sub.2,.s-
igma..sub.2.sup.2)
Where .mu..sub.1 and .mu..sub.2 are the mean values, corresponding
to the latency, and .sigma..sub.1 and .sigma..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.
[0033] 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 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 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.
[0034] 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.
[0035] 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.
[0036] 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 display 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 CDLD display
presents a nerve firing probability in an intuitive and helpful
picture for audiologist.
[0037] 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.
[0038] 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.
TABLE-US-00001 TABLE 1 Median values for physiological NAP
responses Gaussian Scale Latency Standard Component Factor s .mu.
Deviation .sigma. 1. 0.32 0.47 ms 0.19 ms 2. 0.71 1.01 ms 0.37
ms
[0039] 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: [0040] Stimulation
amplitude [0041] Inter-pulse interval during a recovery sequence
[0042] Masker and stimulation level during a recovery sequence
[0043] Polarity of stimulation pulse [0044] Distance on electrode
array between masker and probe during a spread of excitation
sequence [0045] 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).
[0046] 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.
[0047] 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.
[0048] 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).
[0049] 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.
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