Optimizing Electrode and Filter Selection in Cochlear ... · Optimizing Electrode and Filter Selection in Cochlear Implant Speech Processor Maps Katherine R. Henshall* Colette M

J Am Acad Audiol 12 : 478-489 (2001)

Optimizing Electrode and Filter Selection in Cochlear Implant Speech Processor Maps Katherine R. Henshall* Colette M. McKay*

Abstract

This study examined two hypotheses : that speech understanding of cochlear implantees could be improved by removing electrodes that exhibit nontonotopic percepts from the speech processor map and that speech understanding could be improved by extending the range of high frequencies that are mapped to the electrodes . Electrodes producing nontonotopic per-cepts were identified using a multidimensional scaling procedure with seven users of the Nucleus C122 implant and Spectra processor. Two experimental maps were created with those electrodes removed : the first using the same set of filters as their clinical map and the second using the complete set of filters available. After periods of take-home experience, speech perception was tested and compared for the two experimental maps and their own clinical map. It was found that removing nontonotopic electrodes did not improve speech per-ception, possibly due to the deleterious side effect of shifting the frequency-to-electrode allocation . Also, extending the high-frequency range of the map did not improve speech per-ception, possibly due to the poor sensitivity of this processor to high-frequency sounds .

Key Words: Cochlear implants, speech perception

Abbreviations: ANOVA = analysis of variance, CNC = consonant-nucleus-consonant, MDS = multidimensional scaling, SIT = Speech Intelligibility Test, SNR = signal-to-noise ratio

P erformance on speech recognition tasks varies widely among the users of cochlear implants . Perceptually, a lis-

tener's ability to perceive the spectral pattern of individual phonemes is an important factor in his or her recognition of speech sounds . This study addresses two factors that may affect how spectral pattern is perceived by cochlear implantees . The first is whether the active elec-trodes are perceived as tonotopic. If there are nontonotopic electrodes, their removal from the map may improve speech perception . The sec-ond factor addressed in this study is whether extending the high-frequency range beyond the range presently allocated to the electrodes in

*Department of Otolaryngology, The University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia

Reprint requests : Katherine Henshall, Department of Otolaryngology, The University of Melbourne, Royal Victorian Eye and Ear Hospital, 32 Gisborne St, East Melbourne, 3002, Australia

clinical speech processor maps is beneficial for speech understanding.

Multichannel implants are assumed to pro-duce a place-pitch percept that is tonotopically ordered, increasing in pitch as electrodes are stimulated sequentially from apical to basal positions. The place-pitch percept is used to effectively code spectral information across a wide range of frequencies (up to 10 kHz), and there are also more limited temporal pitch per-cepts (up to about 300 Hz) that can be evoked by temporal patterns on a single electrode (Shan-non 1983 ; Tong and Clark, 1985). Current speech processing strategies such as SPEAK (Skinner et al, 1994) make use of the tonotopic place-ment of the electrodes simulating the neural excitation patterns of a normal ear. If an elec-trode produces nontonotopic percepts, it may be that the neuron population it excites is more proximal to the population excited by distant electrodes than by adjacent electrodes . This would result in a neural excitation pattern shape that no longer mimics that of normal hearing and may produce a distorted perception of the spec-tral pattern.

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Speech Recognition in Cochlear Implantees/Henshall and McKay

Previous studies have demonstrated that place-pitch is usually tonotopically ordered (Busby et al, 1994; McDermott and McKay, 1994), although these same studies also show that place-pitch is not completely tonotopic in all implantees . Using a pitch ranking task in a group of five subjects, McDermott and McKay (1994) found that two subjects showed evidence of pitch reversals. In one subject, the three most basal electrodes were ranked as significantly lower in pitch than the next four basal-most electrodes . A significantly reversed pitch was evident at the apical end for a second subject. Busby et al (1994) used numeric estimations of pitch to compare the effect of mode of stimula-tion in nine subjects . They reported pitch rever-sal for up to six electrodes in the middle of the arrays in three subjects in common ground mode and also less marked reversals (i .e ., involving fewer electrodes) at the ends of the arrays in bipolar mode in three subjects .

Other authors have compared different meth-

ods for determining pitch perception related to electrode position . Collins et al (1997) used both pitch ranking and pitch scaling with the same group of cochlear implantees and found that the two procedures produced different orderings of

electrode pitches . They suggested that the percept related to electrode place may not be restricted to one dimension (i .e ., pitch), but timbral differ-ences may also be perceived. Experimental maps using the multipeak (MPEAK) speech coding strategy (Skinner et al, 1991) were created by real-locating filters to electrodes using the tonotopic order of electrodes obtained from the pitch rank-ing task . Speech discrimination results showed a deterioration in scores compared with the sub-jects' own maps in most instances, except for

those subjects with only minor deviations from a tonotopic pitch structure. Possibly, improvements might have been seen if subjects had been given take-home experience using the maps .

In a recent study, Collins and Throckmor-ton (2000) used multidimensional scaling (MDS) to assess the perceptual relationship of elec-trode positions in cochlear implantees . In MDS, a perceptual dissimilarity matrix is analyzed to produce a stimulus space in which the spatial distances between the stimuli best match the perceptual dissimilarities of the stimuli. Collins and Throckmorton reported that the configu-ration of the stimuli that best described their per-ceptual relationship was a horseshoe-shaped curve with stimuli ordered by electrode posi-tion and that place pitch was related to the sin-gle dimension of angular position around the

curve . These authors demonstrated that this horseshoe shape, rather than a straight line, may be due to subjects' inability to distinguish between different large dissimilarities . They also showed that if such data (i .e ., which do not distinguish between different large dissimilar-ities) are analyzed in three dimensions, an S-shaped curve is apparent when the horseshoe is viewed side on . Examples of both the horse-shoe- and S-shaped patterns can be seen in Fig-ure 1, which is shown in the "Results" section of this article. Interestingly, even small sets of stimuli produce a curve shape if they are highly discriminable (McKay et al, 1996). Collins and Throckmorton (2000) found that MDS can be used to pinpoint indiscriminable electrodes more accurately than can pitch ranking data . Previ-ously, McKay and Carlyon (1999), also using MDS, reported that the percepts evoked by dif-ferent rates of stimulation also produce a horse-shoe-shaped configuration of stimuli in a two-dimensional stimulus space, arranged in the order of rate pitch.

In this study, we investigated the effect on speech perception of deactivating electrodes pro-ducing anomalous percepts . MDS was used to determine the perceptual order of subjects' elec-trodes and to identify which, if any, of the elec-trodes were perceptually nontonotopic . Electrodes that produce nontonotopic percepts are evident in an MDS stimulus space as they do not follow the expected order around the curve or do not lie on the curve at all if they pro-duce a percept differing in a dimension additional to place pitch. Electrodes may produce auditory percepts other than pitch, such as timbral dif-ferences, or possibly tactile percepts such as facial nerve stimulation. How electrode dis-criminability affects speech perception in cochlear implantees is also a relevant question, but only those that were nontonotopic or pro-duced atypical sensations were deactivated in this study.

The second issue examined in the current study was whether extending the upper fre-quency limit in subjects' maps would improve their speech perception . The fitting software for the Cochlear Ltd. C122 implant has 15 frequency tables from which it automatically assigns fil-ters to electrodes depending on the number of active electrodes available (Cochlear Ltd., 1994). Three Cochlear Ltd. frequency tables, 7, 9, and 10, are shown in Table 1. Theoretically, the range of 150 to 10,823 Hz will be accessible to a cochlear implantee with 20 functioning elec-trodes using Cochlear's default frequency table,

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Journal of the American Academy of Audiology/Volume 12, Number 9, October 2001

Table 1 Cochlear Ltd. Frequency Allocation Tables

Table 7 Frequency (Hz)

Lower Upper


Lower Upper


Lower Upper

120 280 150 350 171 400 280 440 350 550 400 628 440 600 550 750 628 857 600 760 750 950 857 1085 760 920 950 1150 1085 1314 920 1080 1150 1350 1314 1542 1080 1240 1350 1550 1542 1771 1240 1414 1550 1768 1771 2020 1414 1624 1768 2031 2020 2321 1624 1866 2031 2333 2321 2666 1866 2144 2333 2680 2666 3062 2144 2463 2680 3079 3062 3518 2463 2856 3079 3571 3518 4081 2856 3347 3571 4184 4081 4781 3347 3922 4184 4903 4781 5603 3922 4595 4903 5744 5603 6564 4595 5384 5744 6730 6564 7691 5384 6308 6730 7885 7691 9011 6308 7390 7885 9238 9011 10557 7390 8658 9238 10823 - -

Table 9 . More recent users of the C122 are often programmed using a different table, Table 7, with the range of 120 to 8658 Hz, as it has been demonstrated that this provides better speech recognition (Skinner et al, 1995). In maps with fewer active electrodes, the frequency range is reduced; for each deactivated electrode, one less filter is used. When, for example, 4 electrodes are deactivated (16 active electrodes), the frequency range is reduced from 150 to 10,823 Hz to 150 to 6,730 Hz in Table 9. Once 5 electrodes are deactivated, the software defaults to a different frequency table, Table 10, and the range is fur-ther decreased to 171 to 5,603 Hz . It is possible for the clinician to choose another frequency table, but the total range of frequencies will still be reduced compared with a patient with 20 available electrodes .

A frequency importance function for the recording of the consonant-nucleus-consonant (CNC) words used in this study has been derived by Henry et al (1998) . It demonstrated the rel-ative importance of frequencies up to 8 kHz for speech understanding in normal-hearing lis-teners and showed that higher-frequency infor-mation (above 5 kHz) contributes significantly to speech information. This result is in contrast to earlier studies and may be due to the use of more modern transducers with a better high-frequency response than those used in older

studies (e.g., Studebaker and Sherbecoe, 1991) and also to the speaker characteristics in the recording. If so, the perception of live speech with a variety of speakers, as represented by every-day listening conditions, will also derive bene-fit from the audibility of information above 5 kHz. These authors have also recently demon-strated that discrimination of high-frequency input (above approximately 3 kHz) is less impor-tant than the audibility of these speech cues for cochlear implantees (Henry et al, 2000). They found that although high-frequency infor-mation conveyed significant speech information to implantees, the amount of information was not correlated with the ability to discriminate elec-trodes allocated to the high-frequency region. In contrast, low- and mid-frequency information was highly correlated with electrode discrimi-nation ability in the corresponding region of the electrode array. In the current study, we hypoth-esized that the inclusion of extra high-frequency filters, allocated to the most basal electrodes, may improve speech perception by improving the audibility of high-frequency information.

This study aimed to determine whether the speech recognition of cochlear implantees could be improved by (1) deactivating electrodes pro-ducing nontonotopic place-pitch percepts and (2) extending the high-frequency range to include the full range of filters available in the subjects' maps .

METHOD

Subjects

Seven subjects participated in this study. Each had developed deafness postlinguistically and received no useful speech perception with hearing aids immediately prior to implantation . All were users of the Cochlear Ltd. C122 implant, and all but one had a minimum of 2 years expe-rience with the Spectra-22/SPEAK speech pro-cessing strategy (Skinner et al, 1994). Subject 4 had used the multipeak strategy (Skinner et al, 1991) for 9 years and was changed to Spec-tra/SPEAK 6 weeks prior to the first speech testing session of this study. Further subject details are presented in Table 2.

Psychophysical Testing

For psychophysical testing, subjects wore a speech processor that was interfaced to a per-sonal computer. A custom program was used to control the parameters of the stimuli, run the test procedures, and record the subjects'responses .

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Subject Age

Table 2

Etiology

Subject Details

Years of Profound Deafness Prior to Implantation Years of Implant Use

1 74 Infection 23 14

2 69 Genetic 30 13

3 63 Otosclerosis 21 7 4 61 Fever 42 9

5 62 Otosclerosis 16 11 6 53 Genetic 5 11

7 50 Unknown 2 3

Each stimulus was a pulse train of 250 Hz with a duration of 500 msec. Maximum com-fortable listening levels were first measured for each active electrode, using an ascending tech-

nique, by instructing subjects to keep increas-ing the loudness until it was "loud, but comfortable ." For all but one subject, pulse dura-tion (psec/phase) was used to control the level (with the current level fixed at 189 clinical pro-gramming units) . For subject 6, the current was varied to find the threshold and maximum com-

fortable loudness, with the pulse duration fixed at 185 V,sec/phase . In each case, the method of

level control was the same as used in their clin-

ical speech processor map so that the psy-chophysical stimuli would correspond accurately

to the outputs experienced on different elec-trodes when using their speech processor.

The stimulation on each electrode was then carefully loudness balanced at the comfortable loudness relative to a single reference electrode

in the middle of the subject's array. In the loud-ness balance procedure, the subject was able to

control the level (pulse duration or current, as

above) of the stimulus on the test electrode using

a control knob . The test and reference stimuli

were presented alternately, with a 500-msec silent interval between them . Subjects were instructed to listen to the two sounds and to adjust the loudness of the test stimulus until it

matched that of the reference . The procedure was

then repeated using the test electrode as the ref-

erence, and the reference becoming the vari-able stimulus . The entire procedure was then

repeated, and the results of the four balances

were averaged to set the current (or pulse dura-

tion) value on the test electrode .

The set of loudness-balanced stimuli was

then used in the MDS procedures, following a

method described by Schiffman et al (1981) . To

begin, the subjects listened to the whole set of stimuli to familiarize themselves with the range

of percepts . All possible pairs of electrodes in the

set, including both orders for each pair (380

pairs for a map of 20 electrodes), were pre-sented in a random order . Subjects were asked

to rate the similarity or difference of each pair of sounds by indicating a position along a 10-cm

line, one end labeled "exactly the same" and the

other "the most different ." The position was converted into a number from 0 (indistin-guishable) to 100 (maximum difference), pro-ducing a matrix of perceptual dissimilarity. Each subject had a practice session using this

task before data collection began . The data for

two matrices were obtained for each of four sets of electrodes : one set containing all active electrodes and three containing 10 adjacent electrodes at the basal, mid, and apical regions of the electrode array.

Each pair of dissimilarity matrices was ana-lyzed using repeated-measures nonparametric MDS. As mentioned earlier, the aim of MDS is to arrange the stimuli in a Euclidean space or "stimulus space" in which the rank order of the perceptual dissimilarities matches the rank order of the spatial distances in the stimulus space with the least error (stress) . Stimuli that are perceptually similar will appear close together, and those that are perceptually the most different will appear the furthest apart in the stimulus space. We analyzed the data in a two-dimensional stimulus space, similarly to Collins and Throckmorton (2000) and McKay and Carlyon (1999), to identify any electrodes that evoked nontonotopic percepts . These were defined as those that were on a single horseshoe-shaped (or S-shaped) curve but definitely out of order or those that were clearly not on the curve defined by the stimuli on other electrodes . The all-electrode MDS stimulus spaces were used pri-

marily in this process, and the three 10-electrode curves were used to check for consistency of the data. Those stimuli that were consistently nontonotopic in all spaces in which they appeared were identified, and the corresponding elec-trodes were deactivated in the maps created for the speech perception experiment .

481


Table 3 Map Details

Subject Map Active Electrodes Frequency

Table Frequency Range (Hz) Mode Sensitivity

1 Clinic 123456789101112131415161718 9 150-7885 BP+2 3 MDS 45789101112131415161718 150-7885 MDS+ 457891011 12131415161718 150-10,823

2 Clinic 45678910111214151617181920 9 150-5744 BP+1 3.5 MDS 6891011 1214151617181920 150-5744 MDS+ 6891011 1214151617181920 150-10,823

3 Clinic 1234567891011121314151620 9 150-6730 Mixed 3 MDS 345678910111213141620 150-6730 MDS+ 345678910111213141620 150-10,823

4 Clinic 345678910111213141516171819 7 120-5384 BP+2 2.5 MDS 456810111213141516171819 120-5384 MDS+ 456810111213141516171819 120-8658

5 Clinic 12456789101112131516172022 9 150-6730 CG 3 MDS 12456789101113152022 150-6730 MDS+ 12456789101113152022 150-10,823

6 Clinic 1234567891011121314151617 7 120-5384 BP+3 4 MDS+ 1234567891011121314151617 120-8658

7 Clinic 67891011 121314151617181920 10 171-5603 BP+1 2 MDS 67891314151617181920 171-5603 MDS+ 67891314151617181920 171-10,557

The electrode numbers refer to the electrode ring (numbered basal to apical) that is the most basal of a bipolar electrode pair. The bold numbers in the clinical maps indicate which electrodes were removed in the experimental maps . MDS and MDS+ refer to the MDS and MDS + filters maps, respectively, described in the text.

BP = bipolar ; CG = common ground .

Speech Perception Testing

In the speech perception experiment, three maps were compared : the subject's own clinical map and two experimental maps . Details of the maps are shown in Table 3 .

Multidimensional Scaling Map

In the first experimental map, perceptu-ally nontonotopic electrodes were deactivated. All other map variables were kept the same as the clinical map: the same frequency table was used, and the same number of filters, which was less than 20 for all subjects, was main-tained . Only one filter is normally mapped to each electrode; as electrodes are deactivated, the mapping software switches off the same num-ber of filters, beginning with the highest-fre-quency filter. In order that the same frequency information was audible in the clinical and the MDS maps, the filters that would otherwise be switched off when the electrodes were deacti-vated were instead mapped to the most basal active electrode. For example, if two electrodes were deactivated in the MDS map, a total of three filters were mapped to the most basal active electrode.

Multidimensional Scaling + Filters Map

This map used the same electrodes and fre-quency table as the MDS map but used the full range of filters available in the frequency table (i .e ., 20 filters for subjects using Tables 7 and 9 and 19 for the subject using Table 10), thereby increasing the range of audible frequencies . One channel was allocated per electrode from the apical end of the array, and as with the MDS map, the remaining high-frequency filters were mapped to the most basal active electrode. If the number of filters exceeded the number of elec-trodes by more than three, the excess filters were allocated to the two most basal active elec-trodes, with four on the most basal electrode. This resulted in effectively wider filter bands on these basal electrodes, and hence any differ-ences found would be due to improved audibil-ity rather than discriminability of the associated frequencies. The map C-level on these basal electrode(s), that is, the maximum current on these electrodes in the speech processor map, was reduced if the extra stimulation made the map sound uncomfortably loud or harsh. However, this was necessary only for subject 6, for whom the level on the basal-most electrode was reduced by five current steps.

482


Table 4 Timing of the Test Sessions with the Three Speech Processor Maps

Week Map

1 Clinical 3 MDS 5 MDS + filters 6 MDS + filters 8 MDS 10 Clinical

Each test session was preceded by at least 2 weeks of take-home experience (except for the initial baseline session with

the clinical map) . The MDS + filters maps were worn for 3 weeks

in total, and the subjects tested in weeks 5 and 6 .

Subjects wore the experimental maps at home for a total of 8 weeks. Each map was worn at home for at least 2 weeks prior to a test session. Table 4 shows the testing schedule ; an ABCCBA order of testing was implemented in order that learning effects could be mini-mized as a confounding factor . In each test session, scores on a CNC word test and the Speech Intelligibility Test (SIT) sentences (Mag-ner, 1972) were measured . The CNC word lists used were randomly chosen from the 50 lists

adapted for Australian use by Henry et al (1998), which use the same phoneme set as the original lists of Peterson and Lehiste (1962) . The SIT sentences used were an Australian recording of 40 lists of the original SIT sen-tences ; each list comprised 15 sentences of between three and eight key words . All speech materials were presented at 70 dB SPL with the subjects' processor set at their preferred sen-sitivity (shown in Table 3) and held constant throughout the experiment .

In each test session, two CNC word lists were presented in quiet for all subjects . During the first test session, a list of SIT sentences was presented in quiet using the subjects' clin-ical maps for all but one subject. Only CNC scores were measured for subject 4 as she could not perform the sentence test above chance lev-els. The two other subjects whose score on the sentence test was less than 50 percent (subjects 1 and 2) were tested using two lists of SIT sen-tences in quiet in each subsequent test session. For those subjects who scored greater than 50 percent (subjects 3, 5, 6, and 7), a speech recog-nition task in noise was performed in which the noise level was adapted to find the signal-to-noise ratio (SNR), which caused a reduction in the subjects' scores on the SIT sentence test to 70 percent of their original score in quiet. The adaptive procedure was based on a method

described by Levitt (1970). The speech stimu-lus was set so the average meter reading for key words was 70 dB SPL (fast scale) . The first sen-tence was presented in speech masking noise at an SNR of 21 dB. The percent correct score (key words) for that sentence was calculated and compared with the target score (70% of the score in quiet) for that sentence . Where the actual score was lower than the target score, the noise level was reduced, and where it was higher, the noise level was increased, and the process was repeated for each subsequent sen-tence. The step sizes for the noise level were 5 dB until two reversals were recorded and 3 dB until a further six reversals were recorded . The SNR was averaged for the last six turning points . Two measurements of this SNR were obtained in each test session.

RESULTS

Psychophysics

The stimulus space that resulted from MDS analysis on the full electrode set for each sub-ject is shown in Figure 1. It can be seen that, in general, the results show a horseshoe shape in the two-dimensional space as expected . However there are two exceptions . Subject 2's stimulus space is S-shaped . Collins and Throckmorton (2000) have described this pattern (see the intro-duction), which can also occur if a subject is unable to discriminate between differences of more than a certain degree . In this case, we have accepted the position along the S-curve as representing the tonotopic order of electrodes and have used the three 10-electrode stimulus spaces to identify electrodes that are consistently nontonotopic . Subject 5's stimulus space shows a group of stimuli (electrodes 12, 16, and 17) that do not lie on the horseshoe and therefore do not fit into a one-dimensional model of stimulus percepts .

For each subject, the electrodes removed from the map for the speech perception experi-

ments are marked in bold . These electrodes are either showing nontonotopic behavior or, as with

subject 5, may have another, perhaps nonpitch, percept associated with them . For example, sub-

ject 3's stimulus space shows that, perceptu-ally, electrodes 1, 2, and 15 are nontonotopic

and that electrodes 1 and 2 appear to "fold back"

so that electrode 1 is indistinguishable from

electrodes 7 and 8 . Note that this subject appears to have several groups of hard-to-discriminate electrodes, but only electrodes that were nontono-

483


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topic were removed in this experiment . The eti-ology for both subjects 3 and 5 is otosclerosis, and it is possible that the extra bone formation in the otic capsule has created abnormal current flow leading to these nontonotopic percepts . Subject 6 did not have any electrodes removed from her clinical map. Although the stimulus space with all electrodes (shown in Fig. 1) showed her api-cal group of electrodes to be less discriminable than the basal electrodes, the 10-electrode stim-ulus space with the apical group of electrodes did not show any nontonotopic electrodes in this group .

Effect on Speech Perception of Removing Nontonotopic Electrodes

The effect of removing nontonotopic elec-trodes can be assessed by comparing the clini-cal map to the MDS map. Note that subject 6 did not have any electrodes removed in her MDS map and so is not included in this part of the analysis . Figure 2 shows the CNC word scores for the clinical and MDS maps. These scores were analyzed using two-way analysis of vari-ance (ANOVA) with subject and map as fixed fac-tors . The map effect was nonsignificant (p =

Figure 1 Stimulus spaces derived from MDS for the seven subjects. The numbers refer to different electrode posi-tions. The numbers marked in bold refer to electrodes that were removed from the exper-imental maps . These were con-sistently nontonotopic in these stimulus spaces and in the 10-electrode spaces . Note that, for clarity, where electrodes would otherwise appear superim-posed on one another, they have been spread out slightly in the figure.

.10), indicating that there was no overall effect of removing nontonotopic electrodes ; however, there was a significant interaction effect (p = .04), indicating that the effect of removing electrodes differed among the subjects . Individual results were analyzed with t-tests and showed a sig-nificant difference between the two conditions only for subject 5. His results showed a signifi-cant deterioration when nontonotopic electrodes were removed (p = .02) . Inspection of Figure 2 shows that this subject appeared to be the cause of the significant interaction effect, and this was supported by reanalyzing the data for the remaining six subjects without his data ; with-out subject 5, there was no map effect (p = .75) and no interaction effect (p = .44) .

It seems possible that the deterioration in subject 5's performance was due to the region of the array in which the electrodes were deac-tivated rather than the number of electrodes removed. Examination of Table 3 shows that this subject differed from the others in that the three electrodes removed were from the mid to apical (numbers 17, 16, and 12) rather than the basal region of the array. Because of the way filters are allocated to electrodes starting at the most apical electrode, removal of electrodes


80-

70-

60-

5 0 -~

40-

30-

20--10

0- z

E-1 Cl inic Map MDS Map

z

4 5 7 Mean Subjects

Figure 2 Individual and pooled mean CNC word scores (with standard errors) for six subjects comparing their clinical and MDS maps . The significant individual result for subject 5 is shown by an asterisk .

in the apical region causes a shift in the fre-

quency-to-electrode allocation at more basal points on the array. The other subjects who

showed a (nonsignificant) deterioration in per-formance, 7 and 3, also had mid to apical elec-

trodes deactivated . Other subjects, such as

subject 1, had up to four electrodes removed

without any apparent deterioration in speech

perception . The results for the SIT sentences are shown

in Figure 3. Two subjects, 1 and 2, were tested in quiet using the SIT sentences. No significant differences were found between their clinical map and MDS map scores (p = .125), consistent with their results for CNC words, and no inter-action effect was present (p = .66) . The remain-

60

50-0 U -

40-'° 0 3 -

30-

0 20-

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SIT Score (in Quiet)

ing three subjects, 3, 5, and 7, performed the SIT

sentences in noise using the adaptive task . Their

performance may have been poorer when elec-

trodes were removed, but there was a high

degree of within-subject variability in this task,

with no individual subject having a significant

map effect . A two-way ANOVA found no signif-

icant difference between the maps (p = .15) and

no interaction effect (p = .79) . These results,

although nonsignificant, are perhaps consistent

with the similar drop in performance seen in the CNC phoneme test for the same three subjects

and the same maps .

Effect of Adding Extra High-Frequency Filters

The effect of including extra high-frequency filters can be assessed by comparing the MDS and MDS + filters maps . Figure 4 shows the CNC word scores for the MDS map and the MDS + fil-ters map. A two-way ANOVA found no significant map (p = .49) or interaction effect (p = .10), indi-cating that adding extra filters to extend the high-frequency range had no significant effect for these subjects .

To investigate further this lack of effect for adding high-frequency information above 5 kHz, the output of individual electrodes in response to CNC word stimuli was recorded in this labo-ratory. These recordings indicated little, if any, output above 8 kHz for words at 70 dB SPL, apart from some occurrences of the phoneme /s/ .

Figure 5 shows the results from the SIT sen-tences comparing the MDS and MDS + filters

Signal-to-Noise Ratio (dB) Clinic Map

z " 15 2. En

(D

Figure 3 SIT sentence results comparing the clinic and MDS maps for five sub-jects. The left-hand side shows

the individual and pooled mean scores (with standard errors) for the two subjects who per-

formed the sentences in quiet.

TO The right-hand side shows the w individual and pooled mean

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25

a. t7~

SNR (in dB), which resulted in

a reduction in performance to 70 percent of their scores in quiet, for the 3 subjects who performed this task.

1 2 Mean 3 5 7 Mean Subjects


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70-

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40-

30 -

20-

10

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MDS Map

MDS+Filters Map T,=

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1 2 3 4 5 Subjects

maps, which were analyzed with two-way ANOVAs . The two subjects who were tested in quiet showed no benefit from the extra filters (p = .13), and there was no significant interac-tion effect (p = .10) . Subjects who performed the SIT sentences in noise using the adaptive pro-cedure showed a possible improvement with extra filters, but these results were also non-significant (p = .10), with no significant inter-action effect (p = .93) .

DISCUSSION

Effect of Removing Nontonotopic Electrodes

In the first experiment, it was hypothe-sized that nontonotopic electrodes were pre-sent in the maps of some cochlear implantees and that the removal of these electrodes would improve speech perception . However, for these subjects, we did not find any improvement in speech perception, and, in one case at least, a significant deterioration in performance was evident. It would appear, therefore, that any benefit to be gained by removing these elec-trodes is countered by other factors that reduce speech perception .

One possibility might be that removing elec-trodes from a subject's map would in itself pro-duce a deterioration in speech perception because of the reduced number of stimulation channels available. However, this would not explain the data as the changes in speech recognition were not correlated with the number of electrodes removed. Subject 1, for example, had the max-

z

T

Figure 4 Mean CNC word scores (with standard errors) for all seven subjects showing the MDS and MDS + filters map scores . Note that for sub-ject 6, the MDS map was equiv-alent to her clinical map.

L, 6 7 Mean

imum number of electrodes deactivated of the subject group (four electrodes) but showed no sig-nificant changes in speech recognition.

Another possible source of reduction in speech understanding is the shift in frequency-to-electrode allocation that occurs when elec-trodes that are not on the basal end of the array are removed. Subjects 5 and 7 both complained that voices sounded high pitched and less clear with the experimental maps . Both of these sub-jects (and to a lesser extent subject 3) had elec-trodes removed from the formant regions of their arrays, that is, from apical regions normally allocated filters below about 2.3 kHz, whereas with the other subjects, it was in the more basal region that electrodes were deactivated. When electrodes are removed, the original filters are reallocated to the next available electrode in the basal direction, leading to a basal shift in fre-quency allocation at all points more basal than the removed electrode. Thus, the shift in fre-quency-to-electrode allocation caused by remov-ing an electrode will depend on the position of the removed electrode.

The effects of changing the frequency-to-electrode allocation in cochlear implantees have been examined in previous studies (Friesen et al, 1999 ; Fu and Shannon, 1999). Fu and Shan-non (1999) held electrode location constant and shifted the frequency allocations in four-chan-nel continuous interleaved sampler maps, and found the best speech recognition when sub-jects were listening to maps with frequency allo-cations closest to their own clinical map. They suggested that these subjects had adapted to their particular frequency allocation from their


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SIT Score (in Quiet) Signal-to-Noise Ratio (dB) I MDS Map

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own maps and could not readjust to the new fre-quency allocation within the time frame of their study. Friesen et al (1999) have reported that changes in frequency-to-electrode allocation affect vowel recognition more than consonant recognition ; they also found that the best per-formance was achieved when the frequency allo-cation was closest to the subjects' own clinical map for experimental maps of either 7 or 20 electrodes . They suggested that any shift in fre-quency allocation, either basally or apically, reduces performance . Both of these studies sup-port the possibility that the shift in frequency-to-electrode allocation, caused by removing electrodes in this study, caused a deterioration in performance that was dependent on the posi-tion of the removed electrodes .

In the Fu and Shannon (1999) and Friesen et al (1999) studies, speech recognition was tested using new frequency allocations without giving subjects practice with the experimental allocations . Another study by Skinner et al (1995) found that changing frequency alloca-tions could lead to improved speech recognition after their subjects had trialed the experimen-tal maps for 3 weeks. In our study, subjects had a total of 8 weeks using the experimental fre-quency allocation . For the more subtle change of adding the extra high-frequency filters, subjects had 3 weeks in which to adapt. It is pos-sible, however, that these periods were insuffi-cient to allow measurable benefits to occur for some of the subjects, particularly those who were more elderly.

With long-term cochlear implant patients, it is not uncommon for short circuits or other

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Figure 5 SIT sentence results comparing the MDS and MDS + filters maps for six sub-jects. The left-hand side shows the mean word scores (and standard errors) for the two subjects who performed the sentences in quiet. The right-hand side shows the mean SNR (in dB), which resulted in a reduction in performance to 70 percent of their scores in quiet, for the four subjects who per-formed this task . Note that for subject 6, the MDS map was

2 5 equivalent to her clinical map.

problems to arise on electrodes that have pre-viously functioned normally. When this occurs, typically, the clinician would deactivate the prob-lem electrode(s) and allow the software to real-locate the filters. It is evident from our results that care is needed here as switching off elec-trodes can have detrimental effects on speech perception for some patients . The ability to adapt to frequency shifts may depend on the length of time the person has been using a par-ticular map. Further research is needed to address the question of whether speech per-ception would improve when atypical electrodes are deactivated if their frequency-to-electrode allocation is not shifted. This could be achieved by reallocating the filter from the deactivated electrode to an adjacent active electrode (i .e., allo-cating two filters to the adjacent electrode) or, in future processors, by redesigning the filter bandwidths in that region .

Effect of Adding Extra High-Frequency Filters

The second hypothesis of the current study was that increasing the audibility of high-frequency speech information would lead to an improvement in speech perception . Henry et al (2000) have reported that fine spectral dis-crimination may be more important in the vowel formant region than at higher frequencies (above 2680 Hz), and hence that audibility, rather than discriminability, is important for the higher-frequency speech sounds . In this study, we aimed to improve the audibility (but not the discrim-inability) of these high-frequency sounds by


placing the additional filters on the most basal active electrode. No consistent advantage was found for adding high-frequency filters, although our results demonstrate that it is not detri-mental to include extra filters up to around 8 kHz when they are available.

Inclusion of high-frequency filters does not necessarily mean that the corresponding high-frequency sounds are audible. The micro-phone that is used with the Spectra processor has a frequency response that rolls off at 18 dB per octave for frequencies higher than 4 kHz. When the sensitivity setting is set to the stan-dard optimal value, this roll-off in frequency response causes a reduction in the audibility of high-frequency sounds . Henry et al (2000) studied the audibility of speech information in CNC words, and with the Spectra processor, by comparing the threshold of activation of the implant at different frequencies to the range of intensities in 70 dB SPL CNC words at the same frequency. They found that the speech information was 100 percent audible at 4 kHz, but by 6 kHz, only the most intense third of the speech information was available to the implantee. Thus, although adding filters higher than 6 kHz can theoretically make intense high-frequency sounds audible, the possible benefits are minimal . One benefit may be that the /s/ phoneme, as spoken by some women par-ticularly, has most of its energy above 6 kHz and thus may be more often audible when fil-ters between 6 and 8 kHz are included in the map.

It is interesting to note that Frequency Table 7 has a maximum frequency of 8658 Hz, whereas Table 9 has two filters above 8 kHz. It is likely that these last two filters in Table 9 do not provide significant activation on their corresponding electrodes and thus are "wasted" channels in a map. Previous studies (Skinner et al, 1995, 1997) have recommended that Table 7 be used instead of Table 9 . These authors suggested that Table 7 gave superior speech recognition because the speech energy in the first formant region (lower than 1 kHz) was shared over more electrodes . Our results sug-gest that the use of Table 7 instead of Table 9 does not deprive patients of high-frequency information.

CONCLUSIONS

improve speech perception ability. This hypoth-esis was not supported by the data, with at least one subject's speech recognition deterio-rating when electrodes were removed. The most likely cause of possible deterioration when small numbers of electrodes are removed from a map is the resultant shift in frequency-to-electrode allocation when electrodes are removed from the middle to apical regions of the array in the cochlea. Further research needs to be conducted to see if other procedures for removing electrodes, which do not result in frequency shifts, will lead to some benefit for removing nontonotopic electrodes (or other electrodes that need to be removed for other reasons) .

The second hypothesis was that inclusion of additional high-frequency filters in speech processor maps would improve speech percep-tion . This hypothesis was also unsupported by the data, although the inclusion of the filters did not cause any deterioration in speech perception . The most likely reason for the lack of effect of including filters above 6 kHz is that with the pre-sent microphone and sensitivity settings used with the Spectra processor, only the most intense high-frequency sounds are able to activate the implant. Given the inaudibility of significant high-frequency speech information (Henry et al, 2000), improving the high-frequency sensi-tivity of implant microphones and processors is potentially beneficial .

Acknowledgment . This work was supported by the Australian National Health and Medical Research Centre (project grant #970398) and by the Garnett Passe and Rodney Williams Memorial Foundation via a Senior Research Fellowship to the second author. The advice of Dr. Hugh McDermott and the contributions of the cochlear implantees who gave their time generously for these experiments are also gratefully acknowledged .

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