Proceedings of ISon 2016, 5th Interactive Sonification Workshop, CITEC, Bielefeld University, Germany, December 16, 2016

DISCRIMINATION OF TREMOR DISEASES BY INTERACTIVE SONIFICATIONMarian Weger, David Pirrò, Alexander Wankhammer, Robert Höldrich
Institute for Electronic Music and Acoustics (IEM)
University of Music and Performing Arts, Graz, Austria{weger,pirro,wankhammer,hoeldrich}@iem.at
ABSTRACTThis article is structured as follows. First, in Sec. 2, we describe the technical setup as well as some basic data conditioning steps, such as automatic gain control and Principal Component
Analysis. Afterwards, two different tremor sonification methods
are presented: Vocoder-based (Sec. 3) and EMD-based (Sec. 4).
These sonifications are integrated in an interactive audiovisual interface, which is described in Sec. 5. In order to evaluate bothsonifications and the interface, a pilot study has been carried out,
which is presented in Sec. 6. Finally, in Sec. 7, we summarize ourfindings and give an outlook on future work.
Accompanying sound examples can be found on the project
web page [4]. These include stereo recordings of both sonifications
with two patients of each tremor type.

We introduce an interactive sonification approach for the discrimination of tremor diseases. Following up to our previous research,
we developed two new sonification methods of measured 3-axes
acceleration data of patient’s hands. Prior to sonification, the datais conditioned by Principal Component Analysis (PCA) in order toseparate translational and rotational components of the movement.
The first sonification implements a vocoder-based method in whichenergies of relevant frequency bands are used to control individual
amplitudes of harmonically tuned oscillators.
The second sonification approach is based on Empirical Mode Decomposition (EMD). The input signal is decomposed into Intrinsic
Mode Functions (IMFs) whose frequencies and amplitudes control
an oscillator bank.
In order to enhance the distinct rhythmic qualities of tremor signals
in the output, additional amplitude modulation based on various
instantaneous energy measures has been applied for both sonifications.
An intuitive interface allows to switch interactively between bothsonifications and control critical parameters in order to listen to
specific aspects of the observed tremor. The results of a pilot study
indicate that both sonification methods are able to provide relevant
information on tremor data and represent a useful and complementary addition to already available diagnostic tools.

2. DATA AQUISITION AND CONDITIONING
Movement data is recorded by 3-axis accelerometers1 attached tothe patient’s hands and sampled at 1 kHz. The acceleration signal is processed by a DC removal and low pass filter at 70 Hz inorder to cover the typical frequency range of pathological tremor(predominantly 3 – 15 Hz). Both left and right arm sensors areindividually sonified.
As strong amplitude variations can occur between different
measurements, Automatic Gain Control (AGC) is applied to the
input signal at different stages in both sonifications.
The measurement data is further conditioned by a Principal
Component Analysis (PCA). This method from multivariate statistics facilitates the evaluation of high-dimensional data sets. After finding the principal components, it is possible to divide themovement into translational and rotational components [3], [5].
In the context of the presented sonifications only the first principal component PCA1 [n] is used for sonification. It contains onlytranslational components and describes a projection of the threedimensional data onto a one-dimensional vector.2
In both sonifications, however, the ratio between rotational andtranslational components can be optionally made audible throughan additional chorus effect – a slightly delayed playback of thesonification signal (see Sec. 3.1 and 4.2). While this shows noeffect for purely translational signals, an increasing amount of rotational components results in an increased amplitude of the duplicate signal, up to a strong chorus effect for purely rotationalsignals.

1. INTRODUCTION
Tremor is a movement disorder which produces involuntary rhythmic oscillation movements of a body part [1]. As it can be caused
by various neurological diseases [2], a correct diagnosis is needed
as fast as possible to choose the right therapy. Each of those diseases evokes a specific movement pattern which can be recognizedvisually by specialized neurologists. This visual diagnosis, however, is unreliable and common approaches for additional ex-post
analysis of videos or measured sensor data are time-consuming
and can not be easily integrated into daily clinical practice. Realtime sonification of tremor movement data could therefore becomea promising extension to already available diagnostic tools [3].
Similar to [3] we concentrate on the three tremor diseases
parkinsonian tremor, essential tremor, and psychogenic tremor,
which are sometimes difficult to distinguish by traditional methods. Following up to our previous research [3], we developed two
new sonification methods for tremor analysis. These are supposed
to be used interchangeably dependent on tremor characteristics
and personal preference. The presented sonification interface istargeted at interactive use in the presence of the patient, acting as asupplementary medical tool in order to improve diagnostic quality.

1 Biometrics ACL300 (mass: 10 g, range: ±10 G, accuracy: ±2% FS):
http://www.biometricsltd.com/accelerometer.htm
2 A detailed description of the PCA implementation can be found in [3].

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Proceedings of ISon 2016, 5th Interactive Sonification Workshop, CITEC, Bielefeld University, Germany, December 16, 2016
3. VOCODER SONIFICATION

varies around the center frequency. In contrast to the parkinsonian tremor, often only one or two fluctuating harmonics are distinguishable.
The movement pattern of the psychogenic tremor can be seen
as a mixture of both other tremors. Consequently, this applies for
its sound characteristics.

The first sonification implements a vocoder-based method in whichenergies of relevant frequency bands are used to control individual
amplitudes of harmonically tuned oscillators. Eventually, the amplitude of the summed output is modulated by the envelope of theoriginal input signal to reproduce the specific rhythmic behaviorof the tremor.

4. EMD SONIFICATION

3.1. Implementation

The second sonification approach is based on Empirical Mode Decomposition, as was already suggested in [3].

Sonification 1 can be divided into the following functional blocks:
1. Data Preconditioning: bandpass filtering, AGC, PCA

4.1. Empirical Mode Decomposition

2. Division into frequency bands
The signal is divided into 5 frequency bands by using a sliding window FFT:
2–4 Hz, 4–6 Hz, 6–9 Hz, 9–13 Hz, and 13–20 Hz.
The lower frequency bands are chosen to be relatively narrow, as they are expected to contain most energy. The center
frequencies and bandwidths were selected based on experience with the spectra of different tremor types.

EMD was originally developed by Huang, Shen, Long, et al. [6] toanalyze non-stationary and non-linear signals. Complex data sets
can be decomposed into a finite (and often small) number of socalled Intrinsic Mode Functions (IMFs). Each IMF represents one
mode of the signal.
In contrast to the Fourier analysis, where a signal is decomposed into a set of pre-defined base functions, the EMD obtains
the base functions adaptively from the signal. A perfect reconstruction of the original signal is possible via summation of thecontained IMFs and the resulting residual signal (see Eq. 1).
The basic EMD algorithm is explained in [6]–[9]. To be able
to define an extracted function as IMF, two conditions must be
fulfilled:
1. The number of maxima and the number of zero crossingsmust be equal or only different by one.
2. The (current) average, determined through the envelope ofthe maxima and minima, must be zero.

3. Energy distribution in the bands
The energy signals in each band are computed and normalized.
Eventually, by using a variable exponent p, their dynamic
range is expanded (p>1) or compressed (p<1).
4. Oscillator bank
The processed energy values control the amplitudes of fivesinusoidal oscillators tuned harmonically to each other, i.e.,
following the harmonic series (f0 , 2 · f0 , etc.).
• A Frequency Modulation (FM) with the smoothedand half-wave rectified input signal HW {PCA1 [n]}can be applied optionally. This results in a timevarying fundamental frequency of fi (t) instead of aconstant f0 .
• A slightly detuned duplicate oscillator bank is used
for the optional chorus effect.

The sifting process
The process of finding the IMFs xi [n] (1iN ) from the original signal x[n] is called sifting. The input signal is iteratively
decomposed into a finite number of N IMFs. The sifting process
is structured as follows:

5. Summing and Amplitude Modulation (AM)The sum of the five oscillator signals is finally amplitude
modulated by the variably smoothed and half-wave rectified
input signal HW {PCA1 [n]}.

1. Upper and lower envelope generation
Generate the upper and lower envelope based on the local
maxima and minima.
2. Envelope subtraction
Subtract the average of both envelopes mi [n] from the original signal x[n]: hi [n] = x[n] mi [n]3.2. Sound characteristics
Overall, the sonification sounds result in a harmonic complex evoking a clear, optionally time-varying pitch percept (compare soundexamples [4]). The time-varying timbral character resembles vocal
formants whereas the overall amplitude modulation adds a rhythmic dimension. The three different tremor types lead to different
sound characteristics:
A distinct characteristic of the parkinsonian tremor is the very
regular and stable rhythm. Also strength, i.e.amplitude of PCA1 [n]and rhythmic base frequency show only small fluctuations. Due topeakedness of the tremor signal, the energy fluctuations of the different frequency bands are well synchronized and provide a richtimbre.
The essential tremor shows similar rhythmic behavior as theparkinsonian tremor; however, the rhythm is a bit more irregular. Also the frequency of the main peak is less stable and slightly

3. Validity check
Check hi [n] on the validity of the two conditions for IMF.
• If these are fulfilled, hi [n] is an intrinsic mode function xi [n].
• If not, a sifting takes place, which means that steps
1 to 3 are repeated with hi [n] as new input signal.
4. IMF subtraction
Compute residual signal: ri [n] = x[n]xi [n]

5. Termination criterion• If ri [n] is either a constant or a monotonic function,
the sifting is complete.
• If not, ri [n] provides the new raw material for thefurther decomposition process (go to step 1).

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Proceedings of ISon 2016, 5th Interactive Sonification Workshop, CITEC, Bielefeld University, Germany, December 16, 2016
The decomposed signal x[n] can now be written as:x[n] =NX

xi [n] + rN [n]the individual chants is determined by the AM. Different soundcharacteristics can be observed for the examined tremor types:
The parkinsonian tremor leads to singing with constant rhythm
and very stable, and often low, pitch. One IMF often dominates –only one bird is singing.
In case of the essential tremor, the rhythmic pattern is very
similar to the parkinsonian tremor; still, with the addition of somerhythmic disturbances. The audible frequency of the main peak isless stable and fluctuates around the center frequency. This leads
to the impression of eagerly chatting birds at different registers.
Compared to the parkinsonian tremor, the dominant bird/IMF ismore intensely accompanied by others.
As with sonification 1, the psychogenic tremor is hard to identify due to the sound characteristics of both other tremors mixed.

(1)

i=1

Hilbert-Huang Transform (HHT)
For each individual IMF, the instantaneous phase, frequency, andamplitude can be obtained from the Hilbert transform. In conjunction with the EMD, this is called the Hilbert-Huang Transform(HHT) [6].
The HHT carries several advantages compared to other transforms, which have made the EMD a powerful analytic tool. Firstly,
it makes a perfect lossless reconstruction of the original signal possible, while no prior knowledge on the signal qualities (stationary,
non-stationary, etc.) is needed. Further, it provides an illustrationof the “physical world”. Finally, instantaneous attributes can be
determined through the Hilbert transform.
Typical applications of the HHT, amongst others, are medical
tools, damage detection at structures, and analysis of climate data,
earthquakes, or quote time series in financial mathematics [10].
The HHT has been proposed for tremor analysis in recent studies,e.g., [11]–[13].

5. INTERACTIVE USER INTERFACE
The interactive sonification interface is implemented in Pure Data
(Pd). Apart from the sonic representation of the tremor data, a
simple visualization is provided (see Fig. 1).
On the one hand, it shows various visual information, suchas waveform view, oscillogram, level meter, FFT spectrum, ratio
between rotational and translational movement as well as band intensities and IMF frequencies for the individual sonifications.
On the other hand, apart from standard controls, such as volume,
it features interactive access to a selection of sonification parameters. Globally for both sonifications, the smoothing of the AM
modulator signal as well as the optional chorus effect can be controlled. In addition, each sonification allows individual access tooscillator frequency, dedicated gains for left and right arm sensors,and optional FM (only sonification 1).
As we have described, the presented sonification system implements various analysis methods of the tremor input signal. This
tool, however, aims not at providing definite answers nor a final
diagnosis of the disease. Rather, it is employed to extract relevant features of the tremor signal, which are exposed aurally by
the developed sonification algorithms. The resulting feature spaceis high dimensional and therefore predestined for aural renderingin preference to visual representations.
A metaphor for this approach could be the use of the microscope in medical context. By adjusting the magnification and focus, or by adding contrasting agents, it helps exposing and collecting different characteristics of the sample, which would not be
accessible otherwise. These characteristics can then be connected
in order to form a coherent picture. The diagnosis is, and probably
has to be, left to the doctor.
Similarly, the developed sonification interface, whilst aurally
rendering the whole complex feature space, provides fast meansfor contrasting specific features of the tremor against others, e.g.,
by zooming in or out on a particular subset of characteristics. Our
intention is not to break down the complex structure of the input
signal to a lower dimensional or more simple representation, butrather to facilitate the construction of a coherent picture based on
the observed phenomena in order to make informed decisions. We
think that the interactive change of the sonification parameters isparamount for this to happen.
The proposed interactive sonification tool tries to accomplish
this task by combining both visual and aural representations; providing an apt number of toggles and parameters to the user, whichhave an immediate effect on the visual and aural display and allow
rapid switching and comparison between different settings.

4.2. Implementation
In sonification 2, the first five intrinsic mode functions of PCA1 [n]are determined via empirical mode decomposition. As the first,
IMF 1 [n], does not contain much relevant information of the tremor
it is rejected and only the remaining four IMF 2 [n] to IMF 5 [n] areused as an input signal for the sonification. The higher the numberof IMFs, the more low-frequency components are included. Eventually, these IMFs are individually leveled by AGC.
Each IMF then controls the frequency and amplitude of anindividual sinusoidal oscillator. Although the instantaneous amplitude and frequency can be computed at any time by using theHilbert transform, we used a different approach which provided
better sonification quality.
The instantaneous frequencies are determined by generalized
zero-crossing [14], as a computation from the HHT was found toprovide instable results. The determined frequencies are then multiplied by a user-controlled constant factor to map the low tremor
frequencies (about 2 – 15 Hz) to the audible range. Instead of using
the IMF envelope as an amplitude modulator, as proposed by theoriginal EMD algorithm, each oscillator is then individually amplitude modulated by the by the smoothed and half-wave rectified
IMF signal HW {IMF i [n]} itself, in order to display the original
tremor frequency range as a superposition of rhythmic structures.
Similar to sonification 1, a slightly detuned oscillator bank forthe optional chorus effect can optionally be added.
Finally, the output signal of the sonification is formed by thesum of these four signals.
4.3. Sound characteristics
Due to the specific time-varying characteristics of the tremor signals, the sonic result of the EMB-based sonification resembles thesound of singing birds (compare sound examples [4]). The register of each “bird” is dependent on the frequency and amplitudeof the corresponding IMF. The impression of different tempos ofISon2016-3

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Proceedings of ISon 2016, 5th Interactive Sonification Workshop, CITEC, Bielefeld University, Germany, December 16, 2016?

Training
Testing

D

Solve

+

Next Patient

0
+

0
-

1.Tremortype

0
tot

reset

E

oscilloscope

input_data_L

Mode

input_data_R

Psychogenic

Parkinsonian
Essential

3.Patient

Psychogenic

spectrum L

2.Condition

Patient 3

Rest

Patient 4

PosturePatient 5

Action Left

Patient 6

Action Right

Patient 7

Tap: 1 Hz

Patient 8

Tap: 3 Hz

Patient 9

Tap: 5 Hz

Patient 10

stop

mute

Global-Parameters
Modulation
hard

weak

>+12
+6
+2
-0dB
-2
-6
-12
-20
-30
-50
<-99

G

rotation

Sonification-1

H

FM
soft

Translation/Rotationstrongspatial movementJ

volduration [sec]5.28

main peak [Hz]100%

200 [Hz]

L R

118.

20 Hz
10%

oscillator frequencyItranslation

Sonification-2

LK

frequency factor
200

listen to both hands

L R
1

masterAudio LevelF

Patient 2

startspectrum R

Patient 12

3

4

listen to both hands

15 Hz
10 Hz

N

5 Hz

5

band intensities

side

M

vol

side

IMF frequencies

Figure 1: Graphical user interface (here in training mode). Annotations in red letters. Visual feedback: a) waveform view, b) oscilloscope,c) FFT spectrum, each for left and right arm sensor, d) indication of the current ratio between rotational and translational components.
Global parameters: e) compression/expansion of the AM modulator, f) chorus effect. Sonification 1: g) FM modulation, h) oscillator
fundamental frequency, i) visualization of band intensities. Sonification 2: j) oscillator frequency factor, k) visualization of IMF frequencies.
Table 1: The different evaluation types and groups of diseases inthe experiment.

6. PILOT STUDY
The presented sonification methods as well as the interactive interface were evaluated in a pilot study. Under three conditions (seeTab. 1), five test participants (three neurologists, two audio professionals) were asked to identify diseases through sonification alone:
At first, the vocoder-based and the EMD-based sonification were
tested individually, while in the third case participants were allowed to switch interactively between both sonifications. Test participants obtained prior training to differentiate the specific soundcharacteristics and become accustomed to the software interface.
The resulting sound was presented via headphones.
During the experiment, all available parameters (see Sec. 5)
could be altered freely and interactively; however, no data about
this interaction has been collected. It is important to note that
especially the comparison between left and right arm sensor signals revealed critical information on the observed tremor type, e.g.,when listening to both channels simultaneously in stereo. This isdue to the fact that different tremor diseases led to different synchronicity/intensity between both hands. In particular, both arms
are usually similarly affected by essential and psychogenic tremor,
but can show strong asynchronous behavior in case of the parkinsonian tremor [2].
Recorded movement data3 of 30 patients were equally divided

Evaluation
Disease

1
2
3
1
2
3

Sonification 1 (Vocoder)
Sonification 2 (EMD)
Interactive switch between Son. 1 and 2
Parkinsonian tremor (Par)
Essential tremor (Ess)
Psychogenic tremor (Psy)

into three different groups of diseases (10× parkinsonian tremor,
10× essential tremor, and 10× psychogenic tremor). This reference diagnosis was made by means of common clinical diagnosis
criteria.
Each evaluation type was tested in two separate runs. In eachrun, the data of all 30 patients was presented in random order. Participant could only proceed to the next patient after submitting adiagnosis. Afterwards, it was not possible to go back or change aprevious diagnosis. Each started run had to be finished completely(all 30 patients), otherwise the gathered data was invalidated. For
every diagnosis, participants had to specify their confidence on ascale reaching from 1 to 100. Additionally, the elapsed time wasrecorded for each case.

3 The clinical tremor data was collected by the Medical University ofGraz in 2012. Signals were recorded with CED 1401 interface in Spikesoftware and pre-processed in Matlab.

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Table 2: Overview of the results.
Percent correct answers

Evaluation 1 (Voc.)
64.0%

Evaluation 2 (EMD)
60.0%

Evaluation 3 (switch)
57.6%

57.7% – 69.9%

53.6% – 66.1%

51.2% – 63.8%

74.4%
40.7
94 s

82.4%
37.5
75 s

71.2%
38.2
92 s

Confidence Interval CI95

Identical answers (runs 1 and 2)
Confidence
Response time

Table 3: Contingency tables for the three evaluations and the average over all of them. Values describe % of submitted diagnoses
D. R is the reference diagnosis. Correct answers (main diagonal)
are highlighted.
(a) Evaluation 1.
D
R
Par
Ess
Psy
Sum

Ess

Psy

68.6
7.8
27.5
104.0

13.8
66.7
16.3
96.7

17.5
25.6
56.3
99.3

D
R
Par
Ess
Psy
Sum

(c) Evaluation 3.
D

R
Par
Ess
Psy
Sum

Par

Ess

Psy

70.0
2.2
38.8
111.0

11.3
65.6
17.5
94.3

18.8
32.2
43.8
94.7

(d) Average.

Par

Ess

Psy

68.8
8.9
32.5
110.1

10.0
56.7
20.0
86.7

21.3
34.4
47.5
103.2

D

R
Par
Ess
Psy
Sum

76.0%
38.8
87 s

The main diagonal (percent correct diagnosis) as well as thesecondary diagonals (false diagnosis) in Tab. 3d show differences
between the three diseases. On the one hand, patients with parkinsonian and essential tremor were only rarely confused with eachother (11.7% falsely Ess, 6.3% falsely Par); on the other hand,
many of them were falsely assigned to the group of patients withpsychogenic tremor (Par: 19.2%, Ess: 30.7%).
This observation is confirmed by a statistical analysis: The
psychogenic tremor led to noticeably lower values of sensitivity(Par: 0.69, Ess: 0.63, Psy: 0.49), and F-measure (Par: 0.66, Ess:
0.67, Psy: 0.48), compared to both other tremors.

(b) Evaluation 2.

Par

Average
60.5%

6.2. Discussion

Par

Ess

Psy

69.2
6.3
32.9
108.4

11.7
63.0
17.9
92.5

19.2
30.7
49.2
99.1

When interpreting the results of the pilot study, it is important toconsider several aspects concerning the design of the experiment.
The system is expected to be used in real time in clinical practice. Under these circumstances, the information provided by theinteractive sonification interface is supposed to be combined withother tools to form a complete diagnostic chain. Nevertheless, anisolated evaluation was necessary in order to examine its clinicalbenefit.
The results indicate that both sonification methods (Vocoder
and EMD) provide relevant information on the observed tremor toa similar extent and can serve as a useful complement to alreadyavailable diagnostic tools. Both sonifications (Vocoder and EMD)
seem to deliver relevant information on the observed tremor to asimilar extent. A joint usage of both sonifications, however, did
not lead to an improvement in the diagnostic results.
During ensuing discussions with the participants, it came outthat both sonifications were considered equivalent and test participants showed individual personal preference towards one ofthem. An implementation of both systems with free choice therefore seems reasonable.
Due to the similarly high percent correct diagnoses of bothneurologists and audio professionals, it is assumed that the greater
experience with sound, concerning the audio professionals, could
be compensated by the neurologists with their greater experience
with tremors. According to the neurologists, the proposed interface facilitates a detailed insight in the movement pattern of an
examined tremor without visual tools. Consequently, acousticallyobserved characteristics can be associated directly with specifictremor diseases.
Finally, the results showed that the average percent identicalanswers in repeated runs was larger than the actual percent correct
judgments (76.0% vs. 60.5%). It is argued here that specific tremor
characteristics can be recognized robustly over several runs, even
if the conclusion drawn from this observation is “incorrect”. This
fact lets us conjecture that discrimination performance can be further increased by training.

6.1. Results
First results revealed conspicuously low hit rates for some of thepatients. After a following analysis of the data by the neurologists
participating in the experiment, it was noticed that four of thesepatients did not show any tremor during data recording. Another
patient suffered from a very specific disease which does not matchthe typical tremor pattern. As those five data sets could not be
assigned to one of the three investigated tremor categories, they
were excluded from further analysis of the results.
All following results are based on the reduced data set of 25 patients (8× Par, 9× Ess, 8× Psy). Tab. 2 provides an overview ofthese results. On average, correct judgments reached from 58% to
64% for the individual evaluation conditions, which is far above
chance (roughly 33%).
The primary test results (percent correct answers) were analyzed by using a binomial test with one variable “disease” (3 levels), assuming a constant hit rate of 1/3 and sample size 250(25 patients × 5 participants × 2 runs).4 Compared to chance,
the results for all evaluations were highly significantly better (assuming a significance level of 5%). The results of the individual
evaluation forms were not significantly different to each other (seeTab. 2, CI95) and are therefore considered equivalent.
For further analysis of the results, contingency tables were created for the individual evaluation types (see Tab. 3a to 3c). Additionally, the average over all evaluations (Tab. 3d) gives a quick
overview of the correct/wrong diagnosis of diseases.
4 Despite the not exactly equal distribution of patients per tremor disease, calculations were done with a constant hit rate of 1/3.

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References

7. CONCLUSION AND OUTLOOKWe presented an interactive sonification interface for efficient diagnosis of tremor diseases that is intended to be used as a complementary tool in the diagnostic chain.
The evaluation of the two different sonifications showed that
acoustical differentiation between tremor signals is possible andcan facilitate disease classification for various tremor types. The
possibility to interactively switch between the two sonifications
did not improve the diagnostic performance; however, due to diverging personal preference between test participants, an optional
free choice is still found reasonable.
The data analysis can be performed in the presence of thepatient and possibly replaces time-consuming ex-post analysis ofvideo and spectral data. In advantage over those methods, the sonification provides an auditory representation which is continuously
following the spectral characteristics of the tremor and thus allows
to keep track of the time-dependent spectral structures. Due tothe high information density, the sonic result provides rather complex, but still identifiable and discriminable gestalts – especiallywith the EMD-based sonification. On the one hand, this leads to aholistic validation of tremori, while on the other hand, even neurologists with little aural training are able to retrieve different movement patterns in a fast and intuitive way from the sonified tremor
signals. The interactive change of sonification parameters facilitates the construction of a coherent image of the observed tremor
and allows informed decision making. Nevertheless, the proposed
sonification interface is meant for integration into clinical practicein order to extend current diagnostic tools, which is assumed to be
essential for an efficient and correct diagnosis.
For the pilot study, the EMD was performed off-line in Matlab. In case of a future application, however, aiming at fast and reliable diagnosis already during patients’ examination, a real-time
implementation is necessary. This causes some problems: Firstly,
the EMD computation depends on future samples, which automatically introduces a delay in the output. Further, the number ofnecessary sifting loops to find an IMF as well as the number of
IMFs contained in the signal are unknown. The consequent unknown complexity could cause some problems if a limited computing power is assumed. Based on already available solutions,e.g., [15]–[17], we are currently implementing an on-line EMD algorithm which efficiently calibrates its computation parameters to
the signal’s specific characteristics.
As the pilot study was based on a small number of patients(with some of them showing rare atypical tremor movement) andclinical diagnoses were not perfectly reliable, the results of thepilot study are of limited significance. Therefore, we are currently carrying out an extended study with recent data of more than
100 patients with confirmed diagnosis. We assume that aurally
trained test participants can achieve better results than untrained
listeners. It is further argued that neurologists can acquire theseabilities providing that they obtain appropriate ear training. Accordingly, the test participants of the extended study are recruitedfrom an expert listening panel [18], [19], a group of musicians andsound engineers with experience in listening tests. Despite the target audience being neurologists, trained listeners are chosen to ensure a best-case scenario and hence a more fair comparison of theresults with the currently achieved diagnostic accuracy through visual and computer-aided ex-post analysis methods. An additional
focus of the new evaluation is the interactive use of sonification
parameters. First results will be presented at the ISon workshop.

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