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Pathological tremor prediction using surface electromyogram and acceleration: potential use
in ‘ON–OFF’ demand driven deep brain stimulator design
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2013 J. Neural Eng. 10 036019
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IOP PUBLISHING
JOURNAL OF NEURAL ENGINEERING
doi:10.1088/1741-2560/10/3/036019
J. Neural Eng. 10 (2013) 036019 (18pp)
Pathological tremor prediction using
surface electromyogram and acceleration:
potential use in ‘ON–OFF’ demand
driven deep brain stimulator design
Ishita Basu 1,5 , Daniel Graupe 1 , Daniela Tuninetti 1 , Pitamber Shukla 1 ,
Konstantin V Slavin 2 , Leo Verhagen Metman 3 and Daniel M Corcos 4
1
2
3
4
Department of Electrical and Computer Engineering, University of Illinois at Chicago (UIC), IL, USA
Department of Neurosurgery, University of Illinois at Chicago (UIC), IL, USA
Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
Department of Kinesiology, University of Illinois at Chicago (UIC), IL, USA
E-mail: ibjuslc@gmail.com
Received 20 March 2013
Accepted for publication 22 April 2013
Published 8 May 2013
Online at stacks.iop.org/JNE/10/036019
Abstract
Objective. We present a proof of concept for a novel method of predicting the onset of
pathological tremor using non-invasively measured surface electromyogram (sEMG) and
acceleration from tremor-affected extremities of patients with Parkinson’s disease (PD) and
essential tremor (ET). Approach. The tremor prediction algorithm uses a set of spectral
(Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters
extracted from the non-invasively recorded sEMG and acceleration signals. Main results. The
resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in
4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100%
sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials
and 80.2% for all PD trials. By using a Pearson’s chi-square test, the prediction results are
shown to significantly differ from a random prediction outcome. Significance. The tremor
prediction algorithm can be potentially used for designing the next generation of non-invasive
closed-loop predictive ON–OFF controllers for deep brain stimulation (DBS), used for
suppressing pathological tremor in such patients. Such a system is based on alternating ON
and OFF DBS periods, an incoming tremor being predicted during the time intervals when
DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before
tremor re-appears so that the patient is tremor-free for the entire DBS ON–OFF cycle and the
tremor-free DBS OFF interval should be maximized in order to minimize the current injected
in the brain and battery usage.
(Some figures may appear in colour only in the online journal)
main symptoms in PD include tremor, rigidity, imbalance,
and slowness of movement. PD tremor is generally a mild
resting tremor with slow, regular oscillations of 4–6 Hz. It
might also be present during posture or voluntary movements
(postural/action tremor) which is typically in the 7–11 Hz
frequency range. Dopamine replacement therapy, using the
dopamine precursor levodopa is the mainstay of therapy in
early stage PD. However, for advanced PD patients a surgical
1. Introduction
Parkinson’s disease (PD) and essential tremor (ET) are the two
most common progressive neurological movement disorders.
No cure is available at present for either of the diseases. The
5
Present Address: Neurological Surgery, Johns Hopkins Hospital, Baltimore,
MD, USA.
1741-2560/13/036019+18$33.00
1
© 2013 IOP Publishing Ltd
Printed in the UK & the USA
J. Neural Eng. 10 (2013) 036019
I Basu et al
Alternatively, muscular and kinematic signals measured by
means of surface-electromyogram (sEMG) and acceleration
(acc) can be recorded non-invasively from the patients’
symptomatic extremities. These signals are known to carry
predictive information on tremor reappearance [7, 8] and can
thus be potentially used for closed-loop ON–OFF control of
DBS.
The design of a closed-loop DBS controller has been a
highly pursued field over the past decade because of the fact
that although the current paradigm is highly successful, it is
not adaptive to the patients’ condition. Some of the significant
work towards this goal [9] include optimizing the stimulation
pattern, such as phase resetting and delayed feedback
[10–12], using a pulse train with random frequency [13]
and use of local field potentials as feedback for the design
of a closed loop DBS system [14] . However the results of
these efforts are solely based on computational models and
if tested on human subjects, would require measurement of
neuronal signals from the implant site in the brain, which
has the drawbacks discussed earlier. In a study on two
parkinsonian (MPTP treated) non-human primates [15], the
authors showed that cortico-pallidal closed-loop DBS has a
significantly greater effect on akinesia and on cortical and
pallidal discharge patterns than standard open-loop DBS. In
all the trials, the stimulus was applied through two electrodes
located within the GPi in response to an action potential in GPi
or primary motor cortex (M1). Hence this system involves
multiple electrodes instead of the usual clinically used one
electrode.
Our approach consists of updating the existing FDA
approved DBS system by using external non-invasively
measured signals, such as sEMG and acc, as feedback signals
to predict re-emergence of tremor when DBS is OFF. Since
this does not modify the actual stimulation generator and the
implanted electrodes, it can be implemented as an add-on
sub-system for the FDA-approved DBS systems. A schematic
of such a system is shown in figure 1 in which, the signal
acquisition, processing and ON/OFF command generation can
be integrated in a microchip housed inside the sensor assembly
of figure 1 [16] mounted non-invasively over the measurement
site as a wrist-band type housing (protected under patent
application 8391986 approved in December 2012). The control
ON/OFF command is sent via radio frequency link to the
switch assembly which, in turn, switches DBS ON and
OFF via an elecrtromagnetic (EM) induction coupling to the
EM inductor coil commonly used in all present implanted
pulse generator (IPG) devices (for receiving ON–OFF manual
commands from physician or patient, from a manual switching
device such as Medtronic therapy controller Model 7438).
In this paper, we explore the possibility of an external
signal based closed loop ON–OFF type DBS system by
designing a tremor prediction algorithm which can be
implemented in the signal processing unit of such a system
as outlined in figure 1. Such a tremor predictor must achieve
the following objectives.
procedure called deep brain stimulation (DBS) can provide
significant benefit for all motor symptoms while reducing or
eliminating dyskinesias and improving quality of life [1, 2].
The Food and Drug Administration (FDA) approved DBS for
PD in 2001.
ET is characterized by a tremor of 4–12 Hz, that is
present only when the affected muscle is exerting effort
(postural/kinetic tremor) [3]. Treatments that give relief and
improve quality of life in ET patients include drug therapies
such as propranolol and primidone as well as surgical
procedures, such as thalamotomy (an irreversible lesion in a
small part of the thalamus) [4] and DBS [5]. The FDA approved
DBS as a treatment for ET in 1997.
DBS uses a surgically-implanted battery-operated
medical device that delivers high frequency electrical
stimulation through implanted electrodes to target areas
in the brain that control movement [6]. A DBS system
consists of three components: the lead, the extension, and
the neurostimulator. The lead contains four thin insulated
electrodes whose tips are positioned within the targeted brain
area (electrodes 0, 1, 2, 3). The neurostimulator, similar to
a cardiac pacemaker, is implanted under the skin below the
collarbone or over the abdomen. The extension is an insulated
wire that is passed under the skin and connects the lead to
the neurostimulator. The preferred targets in the brain for
placement of a DBS lead are the internal segment of globus
pallidus (GPi) and the subthalamic nucleus (STN) for PD
patients, and the ventral intermediate nucleus (VIM) of the
thalamus for ET patients.
The only FDA approved DBS system for PD/ET is
manufactured by Medtronic, Inc. Their Activa system operates
open-loop. The clinician chooses the optimal electrode
combination and sets the stimulation parameters (pulse
amplitude, duration and frequency), based on subjective and
objective clinical observations to ensure that the patient
receives maximal benefit and minimal side effects. DBS
is provided continuously over time and the stimulation
parameters remain constant over time until the next visit of
the patient to the clinician. Thus, the current DBS technology
is neither adaptive to the patients’ needs nor to the patients’
disease progression over time.
In order to adapt to the patients’ condition, current
DBS systems must be redesigned so as to include a closedloop feedback control where the patients’ symptoms are
continuously monitored and the stimulation is adapted in
response to its variations. To design a closed-loop DBS system,
it is necessary to find a suitable physiological signal that can
be easily measured and has predictive information on tremor
reappearance once DBS is OFF. One such feedback signal
could be the actual neuronal brain activity measured from
individual neurons (micro-recording) represented by the cell
firing, or a group of neurons (macro-recording) represented
by the local field potential, at the site where the DBS
electrodes are implanted. However, the measurement of these
signals by means of DBS electrodes (during DBS OFF times)
require changes to the current FDA approved DBS electrode
and pulse generator system. It would also require decoding
brain activity and simultaneous sensing-stimulation protocols.
• Goal 1: tremor onset should be predicted just a few
seconds before it is actually detected so that the patient
does not experience any discomfort due to tremor.
2
J. Neural Eng. 10 (2013) 036019
I Basu et al
Figure 1 A schematic of a closed loop ON–OFF DBS system that uses external non-invasively measured signals for control. The ON–OFF
switch turns DBS OFF at pre-determined delay (optimized per each patient) and turns it ON when tremor is predicted via a command
through the RF link from the signal processing unit. The switching assembly is non-invasively placed above implanted pulse generator, at
location where physician/patient presently performs manual ON–OFF switching as required. Note: EM–electromagnetic.
• Goal 2: voluntary movement and posture initiation in the
absence of tremor should not be predicted as tremor.
work [8], [18] we did not have acceleration data, we did not
describe the prediction algorithm in full detail and used only
simple preprocessing (wavelet coefficient [8], wavelet entropy
and approximate entropy [18]). An extended Kalman filter
approach was used in [19] to estimate the amplitude and phase
of pathological tremor. The objective was to design a real time
tremor detection algorithm in an ET patient using sEMG and
acc. Although this work was for tremor compensation using
functional electrical stimulation of the muscles, it has the same
flavor as our work. This approach was able to suppress tremor
by 57% and was tested in only one ET patient. Limited number
of parameters in the above mentioned work are not enough to
predict tremor for a wider patient population with different
pathologies and varying extents of tremor. Hence we extend
our previous work by employing additional parameters, as
described in section 2.4. This not only extends our previous
prediction algorithm to PD but also allows for improving
performance in ET. To the best of our knowledge, this is the
first study with a considerable number of patients with varying
degrees of tremor, tested for such an application.
This will produce a novel non-invasive add-on system to
the existing DBS device, that will turn the stimulation ON for
a fixed time interval (optimized for each patient), switch it
OFF and track a set of parameters calculated from the sEMG
and acc in real time. It will automatically turn DBS back ON
before the tremor re-appears such that the tremor-free DBSOFF duration is maximized. We chose to use tremor as an
indicator for turning DBS ON because it is the only symptom in
ET and the symptom that re-appears the fastest after switching
DBS OFF in PD patients with dominant tremor [17].
Our algorithm uses a set of parameters extracted from
sEMG and acc signals to predict tremor onsets. It is to be
noted that our objective is to predict tremor and not detect it.
Hence, we require more than basic spectral parameters which
can predict tremor before it actually occurs. At the same time,
we also need to avoid movements being predicted as tremor.
We show that the designed prediction algorithm successfully
predicts tremor (goal 1) during DBS-OFF period for all the
trials considered (with data collected from human subjects).
At the same time it does not predict too early (goal 2) for
85.7% of the ET trials and 80.2% of the PD trials.
Previously, we have shown that the lower frequency
bands of the sEMG signal, reconstructed by using a discrete
wavelet transform (DWT), contains predictive information
about tremor re-appearance in an ET patient with DBS
implants, after the stimulation is switched OFF [8]. We also
showed that by using a combination of two types of entropy
measures, tremor onset could be successfully predicted from
sEMG recordings from an ET patient [18]. While both [8]
and [18] are concerned with ET patients only, the present
work also covers PD patients. Furthermore, in our previous
2. Methods
2.1. Subjects
Eight patients were recruited for this study. Four PD and two
ET patients were recruited from the Movement Disorder Clinic
at Rush University Medical Center and two ET patients from
the University of Illinois at Chicago hospital. Patient details are
listed in table 1. Informed consents for this study’s protocol
approved by the IRB of respective institutes were obtained
from all patients. The four PD patients had DBS electrodes
(Medtronic DBS lead model 3389) stereotactically implanted
3
J. Neural Eng. 10 (2013) 036019
I Basu et al
Table 1. Patient details.
DBS parameter
Patient
Age
Gender
PD1
PD2
PD3
PD4
ET1
ET2
46
45
52
60
64
67
M
M
F
F
M
M
ET3
ET4
51
62
M
F
Amplitude
(Volts)
Frequency
(Hz)
Pulse width
(μs)
Active
contacts
DBS
implant
Hand
tested
2.8
2.5
2.8
2
2
1,1.4
(L,R)
2.3
2
180
185
185
145
150
130
80
60
120
60
90
120
2008
2002
2004
2009
2002
2010
R
L
R
R
L
L,R
185
185
60
90
1−0+
1−2−C+
0−C+
1−3−C+
2−C+
1−C+(L),
5−6−C+(R)
9−C+
0−C+
2010
2007
R
R
in the STN while the four ET patients had the electrodes placed
in the VIM of the thalamus. All patients had significant tremor
in one or both arms and their symptoms were well controlled
by a combination of stimulation and medication. All of them
have their stimulation on for the entire day as well as at night
while sleeping.
target to enable the subject to maintain the wrist in a neutral
position. sEMG and acc were measured with the patients in
the following three states.
R: Resting, with the forearm and hand muscles completely
relaxed and the hand dangling unsupported over the edge
of the supportive surface as in figure 2(left).
P: Posture, by maintaining the wrist and hand in a neutral,
extended position while keeping it level with the table
surface as in figure 2(right).
A: Action, performing some voluntary action/movement,
such as reaching for the opposite shoulder and/or
extension and flexion of the wrist.
2.2. Experimental setup
All PD patients and three ET patients had one recording session
each and one ET patient had two recording sessions (one for
each arm) in the Neural Control of Movement Laboratory
(NCML) at the University of Illinois at Chicago. On the testing
day, the patients were on their usual medication and a series of
sEMG recordings were obtained from the extensor digitorum
communis (EDC) and the flexor digitorum profundus (FDP) of
the forearm with worst tremor. For one ET patient, sEMG was
recorded from both forearms (over two recording sessions).
The EDC is the muscle that produces extension at the wrist
and fingers, and muscle activity in the FDP results in wrist
and finger flexion. Electrode placement was determined by
muscle palpation during active wrist and finger extension
and flexion. Correct placement was confirmed by inspecting
sEMG output on a digital oscilloscope. The recording setup
was as in [20, 21]. The sEMG signal was amplified (gain set
to 1 000) and bandpass filtered between 20 Hz and 450 Hz
(Delsys Inc., Boston, MA).The high pass filter at 20 Hz is
an in-built feature of the sEMG sensor in order to reject
low frequency noise and movement artifacts. This setting
however, does not filter out the tremor signal since the tremor
bursts are at 5–12 Hz which can be easily recovered by an
envelop detection. Along with sEMG, acc data were recorded
with a calibrated Coulbourn type V94-41 miniature solid-state
piezoresistive accelerometer. It was taped to the hand (2 cm
proximal to the middle of the first metacarpophalangeal joint).
The accelerometer resolution was 0.01 g. Both sEMG and acc
were sampled at fs = 1000 Hz.
In the beginning of the experiment, the patient was
comfortably seated in an upright position on a chair. A table
with an adjustable height was positioned by the side of the
chair and served as the supportive surface for the subject’s
forearm. The height of the table was adjusted to be level
with the subject’s hand when the wrist and fingers were
extended parallel with the floor. The table served as the visual
2.3. Experimental protocol
Two types of trials were recorded for each patient in R, P and
A.
(i) Baseline data. At the start of a recording session, three
baseline trials each of 30 s were recorded with the patient
in R, P and A, with DBS ON.
(ii) Experimental data. After that DBS was switched OFF for
sometime and then a total of 15 to 32 experimental trials
were recorded in R, P and A. Each experimental trial
was of 50–100 s in duration and consisted of an interval
with DBS ON (20–50 s) followed immediately by the
rest of the trial interval with DBS OFF. During DBS-OFF
periods, the first instant when tremor visibly re-appeared
was noted. This was also verified using a threshold of
0.15–0.2 mm s−2 on the acceleration data for states R
and P.
PD patients were tested under all three conditions (R,
P, A) whereas ET patients only performed P and A since
none of the ET patients had resting tremor. In P and A, the
posture holding/movement was initiated either before or after
switching the stimulation OFF.
2.4. sEMG and acc data preprocessing
For final analysis, only the extensor sEMG was used as by
visual inspection of the data it had a higher signal to noise
ratio than the flexor sEMG especially in the R and P state.
During A, extensor sEMG bursts precede flexor bursts, hence
any predictive information is expected to be obtained from the
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J. Neural Eng. 10 (2013) 036019
I Basu et al
Figure 2. Hand position with sEMG and acc sensors during rest (R) (left) and posture (P) (right).
extensor sEMG before the flexor sEMG. This was also verified
by considering parameters calculated from the flexor sEMG,
whose addition did not improve the algorithm’s performance.
The raw extensor sEMG signal (indicated as x(t )) was first
smoothed by calculating its power over windows of 50 ms
(equivalent to 50 samples) duration that slid over every sample.
The smoothed sEMG signal will be denoted as xs (t ). The
sEMG was smoothed to extract the lower frequency tremor
bursts by averaging out the higher frequency oscillations
inside the sEMG bursts. This is equivalent to rectification and
low pass filtering. A set of parameters were calculated using
windows of 1 s (equivalent to 1000 samples) of xs (t ) with an
overlap of 0.75 s thus producing a sample every 0.25 s after an
initial delay of 1.05 s for the first parameter sample. Following
is a description of all the parameters that were calculated for
PD and/or ET dataset.
(sEMG)
(sEMG)
) at peak frequency (Fmax
) were calculated
Power (Pmax
as:
(sEMG)
=
Pmax
Pj
,
(3)
(4)
Since, the smoothed sEMG signal had most of its power
concentrated in the (0–40) Hz range and we are interested in
the 3–18 Hz range (encompassing the typical tremor frequency
range), only frequency components in the (2–40) Hz band
were considered, that is N = 37 and B = 16 in equation
(3). We omit the 0–2 Hz band to account for the dc value
(sEMG)
is the
and very low frequency movement artifacts. Fmax
frequency in the 3–18 Hz range with the largest power, while
(sEMG)
(sEMG)
is the power at Fmax
. We are interested in the low
Pmax
frequency contents of the smoothed sEMG signal as tremor
components are expected to lie in this band [22]. Note that
(sEMG)
in equation (3) is normalized by the power of the
Pmax
recorded data signal outside the main signal bandwidth of
(sEMG)
must be
interest. This is so because the power at Fmax
compared over different trials/recordings, which might have
significantly different power outside the range of interest. In a
way, the quantity in equation (3) can be interpreted as a ratio
between the maximum power in the bandwidth of interest (3–
18 Hz) and the total power in the frequency band that we are
not interested in (18–40 Hz).
Fourier analysis. Let Pk be the power of a 1 s window of
xs (t ) at frequency bands centered around fk , k ∈ {1, . . . , N}
calculated by using a 512 point Fourier transform, where
( fN − f1 ) is the bandwidth of xs (t ). Then the mean frequency
(sEMG)
(Fmean
) was calculated as:
N
fk Pk
(sEMG)
= k=1
.
(1)
Fmean
N
k=1 Pk
It represents the expected value of the frequency distribution
(sEMG)
depends
over the spectrum range considered. Since Fmean
on the distribution of power at the different frequency points
considered, in presence of tremor or just before tremor starts,
(sEMG)
we would expect that this average frequency (Fmean
) would
have a lower value than the one in the absence of any tremor.
Let ( fB − f1 ) be the bandwidth of interest with B < N.
Let j⋆ be the index of frequency with maximum power in this
bandwidth of interest:
j∈{1,...,B}
j∈{B+1,...,N}
(sEMG)
Fmax
= f j⋆ .
2.4.1. Spectral parameters
j⋆ = arg max {Pj }.
Pj ⋆
Wavelet Analysis. xs (t ) was decomposed into M = 10
frequency bands using a Daubechies4 DWT. Let x j (t )
represent xs (t ) in the jth frequency band, j ∈ {1, . . . , M}.
Mean power in the jth wavelet band, Pj is defined as:
1
|x j (t )|2 .
(5)
Pj =
T t∈T
Pj captures the average signal content in the jth frequency
band over each window of duration T = 1s. The mean
power in the (8–16) Hz band was computed using equation
(5) which will be referred to as P4 . This particular band was
considered because it was seen to contain the most predictive
(2)
5
J. Neural Eng. 10 (2013) 036019
I Basu et al
information [8] which can distinguish between tremor and
voluntary movement. Moreover this band also overlaps with
the typical action/postural tremor frequency band.
a tolerance r at the next point. A lower SpEn(U, m, r) value
reflects a higher degree of regularity.
Each window of xs (t ) was used to calculate sample
entropy SpEn(U, m, r) according to equation (11) with U =
xs (t ), m = 2, r = 0.15σ , where σ is the standard deviation
(std) of the signal window considered. Since the number of
samples in the 1 s window (L = 1000) should be 10m − 30m
[25], m = 2 was chosen. For m = 2, values of r range from
0.1 to 0.25 times the std [26].
2.4.2. Entropy parameters
Wavelet entropy. The (Shannon) entropy is a measure of
unpredictability and is often used to quantify the amount of
order/disorder in a signal. In information theory, the entropy
of a discrete random variable (RV) X is defined as [23]:
H(X ) = −
K
pi log pi
2.4.3. Recurrence quantification parameters. Recurrence
quantification analysis (RQA) [27] involves the computation
of a recurrence matrix (RM) with elements, Ri, j , (i, j) ∈
{1, . . . , P}, P = L − (E − 1)τ , for
U = {x(i), i ∈ {1, . . . , L}} of length L involves the
following parameters: the embedding dimension E, a time
delay τ , a norm · (which could be minimum norm, maximum
norm, and Euclidean norm), and radius r.
RM is then calculated as:
(6)
i=1
where pi = P[X = xi ], i ∈ {1, . . . , K}, is the probability mass
function and K is the number of possible outcomes for X.
Based on equation (6), the wavelet entropy, Hwt (t ) of xs (t ), is
calculated as [24]:
Hwt (t ) =
M
x j (t ) log
j=1
1
.
x j (t )
(7)
Ri, j = (r − Di, j ), (i, j) ∈ {1, . . . , P},
where,
|x j (t )|2
x j (t ) = M
, j ∈ {1, . . . , M}.
2
k=1 |xk (t )|
(8)
Di, j =
x j (t ) represents the normalized power of x j (t ) and hence can
be treated as a probability mass function whose (Shannon)
entropy is estimated by Hwt (t ). This parameter was calculated
only for PD sEMG. For each window of xs (t ), the wavelet
entropy was calculated as the average of Hwt (t ) over the time
window.
dav =
max |x(i + k − 1) − x( j + k − 1)|.
i=1
Bm
i (r),
Am (r) =
L−m
Bm+1
(r).
i
j=1
Di, j
P(P − 1)
(14)
(15)
Each window of xs (t ) was used to calculate R according
to equation (15) with E = 5, τ = 3, r = 0.33. These
values were chosen based on guidelines for parameter selection
outlined in the ‘crptool’ MATLAB toolbox [28]. E was
estimated using the nearest-neighbor methodology, that is
the minimum embedding dimension value which maximizes
system information; τ was estimated by finding the first
minimum in the mutual information function and r was chosen
such that R < 1 [27].
The acc signal was used to calculate the mean frequency
(acc)
(acc)
(Fmean
in equation (1)) and power (Pmax
in equation (3) at
(acc)
peak frequency (Fmax in equation (4)) and used for tremor
prediction in PD.
(9)
for i, j ∈ {1, . . . , L − m}, i = j and let:
L−m
i=1
From Ri, j , the recurrence rate R is calculated as:
1
R= 2
Ri, j .
P i, j
Let:
Bm (r) =
P P
xi = [x(i), x(i + τ ), . . . , x(i + (m − 1)τ )], i ∈ {1, . . . , P}.
k∈{1,...,m}
Bm
i (r) = |{ j : d∞ [xm (i), xm ( j)] r}|
2
(13)
where, is the Heaviside function and xi is a vector of length
m constructed as:
Sample entropy. Sample entropy is calculated as the negative
logarithm of an estimate of the conditional probability that a
data series of a given length that match point-wise within
a given tolerance also match when the length is increased
by 1. The computation of the sample entropy, denoted as
SpEn(U, m, r), for a given time series U = {x(i), i ∈
{1, . . . , L}} of length L involves two input parameters m and
r, which are the pattern length and the similarity criterion,
respectively. SpEn(U, m, r) is evaluated as follows. Let
xm (i) = [x(i), . . . , x(i + m − 1)] for i ∈ {1, . . . , L − m + 1}
be a set of length m vector sequences constructed from U. The
ℓ∞ distance between two such sequences x(i) and x( j) is
d∞ [x(i), x( j)] =
xi − x j
, xi ∈ RE ,
dav
(12)
(10)
2.5. Tremor prediction algorithm
i=1
SpEn(U, m, r) is then defined as:
For both PD and ET, DBS is ON for a fixed time during
each DBS ON–OFF cycle. This fixed stimulation duration
for each patient can be estimated as the DBS-ON duration
that maximizes the average ratio of the delay in tremor reappearance to the total DBS ON–OFF duration [8]. This ratio,
Am (r)
SpEn(U, m, r) = lim − log m .
(11)
L→∞
B (r)
where Am (r)/Bm (r) is the conditional probability that two
sequences that are similar for m points remain similar within
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J. Neural Eng. 10 (2013) 036019
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(a)
(b)
Figure 3. (a) A block diagram showing how the two signals, sEMG and acc are used in the algorithm for ET (top) and PD (bottom). (b)
Classification (left) and prediction (right) for the ET algorithm described in section 2.5.1 and shown in (a).
Rdt is described later in section 2.7. In practice, an optimum
DBS-ON period can be estimated either during a patient’s
clinic visit or during programming of the DBS device. When
DBS switches OFF after this fixed ON duration, the prediction
algorithm starts operating. Figure 3(a) shows an overall block
diagram of the signal flow for the entire prediction process.
The smoothed sEMG and acc are used as inputs to the entire
algorithm block as shown in Figure 3(a). In ET, the smoothed
sEMG and acc are first used to classify which state (P/A)
the patient is in just before and immediately after DBS is
OFF and based on the classifier outcome, a set of parameters
calculated from the smoothed sEMG are tracked until one of
them meets a prediction criterion or the DBS OFF interval
exceeds a preset value, when DBS is switched ON. In PD,
both smoothed sEMG and acc are used to calculate a set of
parameters which are tracked. Whenever one of the parameters
satisfies its corresponding prediction criterion or the DBS
OFF interval exceeds the preset value, DBS is turned back
ON. Following is a detailed description of the algorithm
steps.
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Table 2. Parameter set for ET.
No.
p
Parameter set, SET
Predict tremor at time t
1
SpEn and P4
2
R
3
(sEMG)
(sEMG)
Pmax
and Fmax
No.
1
a
Parameter set, SET
Let (i − 1) is a local min and k < i is a local max over (k − 1, i),
if SpEn(k) ∈ (ηl1 , ηh1 ) and SpEn(i − 1) ∈ (ηl2 , ηh2 )
and P4 (l) > ηp , l ∈ (k − 6, i + 4), t = max(i, l)
Let (i − 1) is a local max and k < i is a local min
over (k − 1, i), R(i − 1) − R(k) ∈ (ρl , ρh ),t = i
(sEMG)
(sEMG)
If Fmax
(i) ∈ ( fl , fh ) and Pmax
(i) > fp , t = i
R
2
(sEMG)
Fmean
(sEMG)
If Fmean
(i) ∈ ( fl , hh ), t = i
No.
p
Parameter set, SPD
Predict tremor at time t
1
SpEn and P4
2
Let (i − 1) is a local min and k < i is a local max over (k − 1, i),
if SpEn(k) ∈ (ηl1 , ηh1 ) and (SpEn(k) − SpEn(i − 1)) ∈ (ηl2 , ηh2 )
and P4 (l) > ηp , l ∈ (k − 6, i + 4), t = max(i, l)
R
3
(sEMG)
(sEMG)
Pmax
and Fmax
4
5
(acc)
(acc)
Pmax
and Fmax
Hwt
Predict tremor at time i
Let (i − 1) is a local max and k < i is a local min
over (k − 1, i), R(i − 1) − R(k) ∈ (ρl , ρh ), t = i
Table 3. Parameter set for PD.
2.5.1. Prediction algorithm for ET.
prediction involves two steps.
Let (i − 1) is a local max and k < i is a local min
over (k − 1, i), R(i − 1) − R(k) ∈ (ρl , ρh ),t = i
(sEMG)
(sEMG)
If Fmax
(i) ∈ ( fl1 , fh1 ) and Pmax
(i) > fp1 , t = i
(acc)
(acc)
If Fmax
(i) ∈ ( fl2 , fh2 ) and Pmax
> fp2 , t = i
If Hwt (i − 1) and Hwt (i) ∈ (hl , hh ), t = i
p
) is tracked. If performing an action/movement
SET
a
(state A), the parameter (Iw2 =1 × SET
) is tracked as
shown in figure 3(b). Here I is the indicator function,
w1 and w2 are weights and can either be 0 or 1.
p
a
SET
, SET
are the set of parameters as described in
table 2 and are a subset of the parameters introduced
in the sEMG and acc data preprocessing (section 2.4).
p
a
(for P) or SET
Whenever, one of the parameters in SET
(for A) meets its corresponding prediction criterion
or the DBS-OFF time exceeds a preset value, the
stimulation is turned ON. The two sets of parameters
and their corresponding prediction criterion are listed
in table 2 and is described in section 2.5.4. A default
preset value is a safety measure to ensure that the
stimulation turns ON after sometime in case the
algorithm does not predict any tremor event. This
value can be decided based on the average time the
tremor takes to come back for a particular patient.
For ET patients, the
(a) Classification of patient’s state (P/A). This is done based
on the sEMG and acc signal power by setting some
thresholds, [ηc1 , ηc2 , ηc3 , ηc4 , ηc5 , λ] as shown in figure 3.
The basic premise is that the power of sEMG and acc
signals is greater while performing an active motion than
during holding a posture, which in turn is higher than at
rest. Additionally, there will be an abrupt change in these
signals when there is a change in state. In particular, the
following steps are done.
(i) If the power of sEMG (xs (t )) over 2 s just before and
1.5 s immediately after DBS is switched OFF is less
than ηc1 , then go to ii). Else the state is A.
(ii) Calculate the power of acc (indicated with Pac ) over
1.5 s immediately after DBS is switched OFF. If
Pac ∈ [ηc2 , ηc3 ], the state is P. If Pac < ηc2 , go to
(iii) else go to (v).
(iii) The sEMG and acc are tracked until acc(tvm ) > λ
where tvm is the time instant when acc exceeds
threshold λ.
(iv) Calculate power of sEMG(xs (t )) over intervals
[tvm , tvm + 0.5] and [tvm + 0.5, tvm + 1] which are
denoted as P1 and P2 respectively. If either P2 < ηc4 ,
or if P2 > ηc4 and P1 < ηc5 , state is P. If P2 > ηc4 and
P1 > ηc5 , state is A.
(v) Over the 1.5 s interval after DBS is switched OFF,
find if at any instant, {tvm : acc(tvm ) > 2λ}. If there
is such an instant then go to (iv) else the state is A.
(b) Based on the classifier output, proceed as follows.
If holding a posture (state P), the parameter (Iw1 =1 ×
2.5.2. Prediction algorithm for PD. For PD, the prediction
is done similarly to that proposed for ET. The main difference
is that there is no preceding classification step as in ET and
a set of parameters extracted from the acc signal is also used
in the prediction. Omission of a state classification is to avoid
confusion between rest tremor and change in state from R to
P/A. Moreover, we would also have to determine three sets
of parameters for the three states in PD thus complicating the
algorithm further. Hence for the PD cases, 1 s after stimulation
is turned OFF, (Iw=1 × SPD ) is tracked; whenever a prediction
criterion is met or the DBS OFF time exceeds a preset value the
stimulation is turned ON. The set of parameters SPD and their
corresponding prediction criterion are tabulated in table 3.
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Table 4. Description of thresholds used for state classification and tremor prediction in ET and PD.
Symbol
ET state classification
ηc1 , ηc4 , ηc5
ηc2 , ηc3
λ
ET tremor prediction
ηl1 , ηh1
ηl2 , ηh2
ηp
ρ l , ρh
fl , fh
fp
PD tremor prediction
ηl1 , ηh1
ηl2 , ηh2
ηp
ρ l , ρh
fl1 , fh1 , fp1
fl2 , fh2 , fp2
hl , hh
Description
Upper thresholds on sEMG power
Lower and upper thresholds on acc power
Lower threshold on acc signal
Lower and upper thresholds for local maximum of SpEn
Lower and upper thresholds for local minimum of SpEn
Lower threshold for P4
Lower and upper thresholds for increase in R
(sEMG)
(sEMG)
Lower and upper thresholds for Fmax
or Fmean
(sEMG)
Lower threshold for Pmax
Lower and upper thresholds for local maximum of SpEn
Lower and upper thresholds for decrease in SpEn
Lower threshold for P4
Lower and upper thresholds for increase in R
(sEMG)
(sEMG)
Lower and upper thresholds for Fmax
, lower threshold for Pmax
(acc)
(acc)
Lower and upper thresholds for Fmax
, lower threshold for Pmax
Lower and upper thresholds for consecutive Hwt samples.
2.5.3. Algorithm parameters and threshold values. All the
trials recorded from each patient were divided into two groups:
(a) training trials consisted of the baseline trial data (30 s
DBS-ON condition in R, P and A) and around 40% of the
total experimental trial data (consists of recordings with DBSON followed by DBS-OFF) as in section 2.3, (b) testing trials
consisted of experimental trials that were not used for training.
The algorithm was tested using just the testing trials as well as
all experimental trials.
For each patient the threshold values for the classification
(ET) and those for parameters used in the prediction algorithm
were chosen as follows. The threshold values for the state
classification in ET were chosen by comparing the sEMG
power and acc magnitude in all three states during DBS-ON
and DBS-OFF intervals of the training trials. All parameter
values as described in section 2.4, were first calculated for
the sEMG and acc signals recorded during the training trials.
The calculated parameters for intervals of no tremor (entire
baseline data duration and tremor-free intervals of the rest
of the training trials) were compared with those in the DBSOFF intervals of the training trials when tremor started to build
up. Based on the difference between the parameter values with
and without tremor, a threshold was decided for each parameter
such that each of the training trial produced a desired prediction
output (TP or TN defined in section 2.6). For parameters
with two thresholds, the second one was estimated based on
difference in the parameters in state R and during movement
initiation. Only those parameters were included in the set as
in table 5, which produced desired output for the maximum
number of training trials using the same threshold.
Table 5. Patient specific parameter set for tremor prediction
algorithm in PD and ET.
PD Patient
Parameters used for prediction
PD1
PD2
PD3
PD4
ET Patient
sEMG
sEMG
SpEn, P4 , R, Pmax
and Fmax
SpEn, P4 , Hwt
acc
acc
Hwt , Pmax
and Fmax
acc
acc
and Fmax
SpEn, P4 , Hwt , Pmax
Parameters used for prediction in P(w1 = 1)
and A(w2 = 1)
sEMG
sEMG
and Fmax
P: SpEn, P4 , Pmax
sEMG
A: R, Fmean
sEMG
sEMG
P: Pmax
, Fmax
A: R
P: SpEn, P4
sEMG
A: Fmean
P: SpEn, P4
sEMG
A: R, Fmean
sEMG
sEMG
P: SpEn, P4 , Pmax
and Fmax
A: R
ET1
ET2(left)
ET2(right)
ET3
ET4
for the most recent local maximum and minimum of
sample entropy. If the maximum precedes the minimum
and each of them or their difference lie between
thresholds, ηh1 , ηl1 and ηh2 , ηl2 , then the algorithm checks
if simultaneously over this entire period the power in the
8–16 Hz wavelet band exceeds a threshold, η4 and predicts
a tremor if all the three conditions are satisfied. This
criterion relies on the fact that during a tremor buildup,
the sEMG will increasingly become more synchronized,
hence leading to a decrease in the sample entropy and an
increase in the 8–16 Hz sEMG signal power.
(ii) Wavelet entropy (Hwt ). This parameter is used only
for PD dataset. The algorithm predicts a tremor if the
wavelet entropy value at that time instant and the one
immediately preceding it lie within the thresholds hh , hl .
The underlying logic is similar to that for sample entropy.
(iii) Recurrence rate (R). At each time instant i, the algorithm
searches for the most recent local maximum and minimum
of recurrence rate. If the minimum precedes the maximum
2.5.4. Prediction criterion. Tables 2 and 3 list the prediction
criterion that each parameter should meet in order for a tremor
to be predicted and the thresholds used in the prediction criteria
are listed in table 4. Following is a brief description of the
criteria and the underlying logic.
(i) Sample entropy and power in the 8–16 Hz wavelet band
(SpEn, P4 ). At each time instant i, the algorithm searches
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Figure 4. Timing points for events from DBS-ON time (ton ) to tremor detection time (ttr ) marked in bold line. There are four possible
scenarios: 1, 2 are TD trials, in 1 the tremor is predicted before its detection (TP/FP) and in two tremor is predicted after its detection (FN);
3,4 are NTD trials, in 3 tremor is not predicted over the entire interval TN and in 4 tremor is predicted FP. Notation: Ttot is the total duration
of a trial, ton and toff are the times when DBS was switched ON and OFF respectively, ttr and tpr are the times when tremor was detected and
predicted using the algorithm, respectively.
threshold on recurrence rate and the 8–16 Hz wavelet band
power are used to discriminate between voluntary movement
and tremor.
and the increase between these values lies between
thresholds, ρl , ρh , a tremor is predicted. This criterion is
based on the increase in sEMG recurrence during tremor
buildup.
(sEMG)
(sEMG)
(acc)
(acc)
(iv) Power at peak frequency (Pmax
, Fmax
, Pmax
, Fmax
).
The algorithm predicts a tremor if at that instant, the
frequency of the sEMG or acc signal, that has the
maximum power lies between certain thresholds and the
power exceeds a threshold as listed in tables 2 and 3. The
frequency range defined by the thresholds is the typical
tremor frequency. This criterion checks if the spectral
component of the sEMG/acc which has the maximum
power is the tremor frequency band as well as if this
power exceeds a certain value.
(sEMG)
(v) Mean frequency (Fmean
). This parameter is used only
for predicting ET kinetic tremor. The instant when the
mean frequency value lies in between thresholds fh , fl ,
a tremor is predicted. It is based on the fact that mean
frequency was observed to lie in a certain frequency band
before tremor starts.
2.6. Classification of prediction outcomes
To analyze the prediction performance, each trial is classified
based on the prediction outcome as follows. Let Ttot be the total
duration of a trial, and ton and toff be the times when stimulation
was switched ON and OFF, respectively. Furthermore, let ttr
and tpr be the times when tremor was detected during the DBSOFF period and tremor was predicted using the algorithm,
respectively as in figure 4. The trials were then classified as:
(1) TD (Tremor detected). These are trials where tremor was
detected over the recorded interval after stimulation was
OFF, i.e ttr < Ttot .
• If [(ttr > tpr ) and (ttr −tpr ) < max(5 s, 0.4(tpr −toff ))]
or [(ttr < tpr ) and (tpr − ttr ) < 1s], then the algorithm
successfully predicts tremor and this outcome is
classified as a true positive (TP).
• If (ttr > tpr ) and (ttr − tpr ) > max(5s, 0.4(tpr − toff )],
then the prediction is too early and the outcome is
classified as false positive (FP).
• If (ttr < tpr ) and (tpr − ttr ) > 1s, then the prediction is
too late and the outcome is classified as false negative
(FN).
The TP definition is a bit different from the classical one,
in that we require that the prediction be at most 40% of the
tremor free DBS-OFF period or 5 s (whichever is greater)
before actual tremor reappears. This allows penalizing
too early prediction outcomes. The maximum between
40% of (tpr − toff ) and 5 s is considered to account for
Use of a lower threshold for entropy and spectral
parameters and an upper threshold for R help reduce false
predictions due to voluntary movements. This is based on
the assumption that a movement initiation causes a higher
degree of synchronization and hence a greater magnitude of
decrease/increase in parameter values than tremor. It was also
seen that the 8–16 Hz band carried more tremor information
than movement artifact. On the other hand, both tremor and
movement cause an increase in the lower frequency band
(1–8 Hz) power. Hence, the wavelet band power along
with sample entropy also serves to discriminate voluntary
movements from tremor. Thus, the lower threshold on sample
entropy, wavelet entropy, mean and peak frequency; the upper
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were completely random. The p-value is the probability that
X > |χ 2 |, where X is a random variable with a χ 2 distribution
with one degree of freedom. A p-value less than α indicates
the prediction outcome is significantly different from a random
prediction with α being the significance level and is often
chosen to be 5% or less.
For this application, S should be very high (over 90%)
because we want to avoid missing any tremor event. At the
same time, we also want to have high A and a low FA. This
ensures that the algorithm not only correctly predicts tremor
events, but also avoids early predictions. The mcc value should
be close to 0.5 or higher and should produces a p-value that is
less than 5%.
For each patient, A, S, FA in equations (16), (17), (18)
and mcc in equation (19) were calculated for all experimental
trials as well as for the testing trials. A χ 2 statistic and the
corresponding p-value was calculated only when the number
of trials exceeded 10 [30]. For patients with NTD< 5, FA
was not calculated (NC). An overall A, S, FA and mcc were
calculated based on all experimental and testing trials in PD
and in ET. Additionally, three ratios are calculated for each
patient which are defined as:
(tpr − toff )/
(ttr − toff ),
(21)
Rpd =
trials where the tremor delay is very short (< 10 s) for
which a prediction 5 s ahead in time is good enough to
be classified as a TP. We also allow for prediction at most
1 s after detection. This will take care of situations when
the tremor re-appears almost immediately (within 1–2 s)
after stimulation is switched OFF.
(2) NTD (No-tremor detected). These are trials where tremor
was not detected over the recorded DBS-OFF interval, i.e
ttr Ttot .
• If the algorithm does not predict any tremor over the entire
interval Ttot − toff , then its classified as true negative (TN).
• If the algorithm predicts tremor over the entire interval
Ttot − toff , then its classified as false positive (FP).
2.7. Statistical analysis of prediction outcomes
For the algorithm to perform well, the total number of TP and
TN must be maximized while minimizing FP and eliminating
FN. This would achieve the maximum ‘tremor-free’ DBS-OFF
interval. To quantify this, the following performance metrics
are defined:
A=
#TP + #TN
,
#TP + #TN + #FP + #FN
#TP
,
S=
#TP + #FN
(16)
Rdt =
(17)
Rpt =
#NTD − #TN
(18)
#NTD
(#TP)(#TN)−(#FP)(#FN)
.
mcc = √
(#TP+#FP)(#TP+#FN)(#TN+#FP)(#TN+#FN)
(19)
A in equation (16) is the accuracy of the prediction
algorithm, which is the ratio of the correctly predicted trials
to the total number of trials. For our application, we aim to
have a high accuracy (above 80%). S in equation (17) defines
the sensitivity of the prediction algorithm. It relates to the
algorithm’s ability to correctly predict tremor in TD trials. This
value has to be very high (as close to 100% as possible) for the
application since we want to avoid missing any tremor event.
FA in equation (18) is the false alarm rate, which expresses
the ratio of NTD trials that are falsely predicted. This relates
to the algorithm’s ability to correctly identify the absence of
tremor when there is no tremor. We aim to have a low FA value
so that the tremor-free DBS-OFF interval is maximized. mcc
in equation (19) is the Matthews correlation coefficient [29],
which is a measure of the quality of a binary classifier. It is
generally regarded as a balanced measure and is used even if
the classes are of very different sizes. It has a value in the range
−1 to 1, where 1 represents a perfect prediction, 0 no better
than random prediction and −1 indicates total disagreement
between prediction and observation. It is related to the chisquare statistic for a 2 × 2 contingency table
2
(ttr − toff )/
(tpr − toff )/
(ttr − ton ),
(22)
(tpr − ton ),
(23)
where the summation is over all the experimental trials for
each patient. Since for the NTD trials the exact time when
tremor would come back is not known, we set ttr = Ttot
and tpr = min(Ttot , tpr ). In a practical scenario, Ttot would
be the time when the stimulation switches ON automatically
in the absence of a tremor prediction. Hence, to determine
the fraction of time the stimulation is OFF, we can consider
Ttot − toff to be the time interval when stimulation is OFF for
NTD trials.
Furthermore, Rpt is calculated only for trials where the
DBS-ON duration, Ton∗ = (toff −ton ), is the one that maximizes
the ratio, R∗ = (ttr − toff )/(Ton∗ ) and is denoted as:
ttr − toff
.
(24)
R∗pt =
tpr − toff + Ton∗
Rpd is the ratio between the predicted delay to the actual delay
in tremor, hence Rpd provides a measure of how good the
prediction is, i.e., a higher value indicates that the predicted
delay is closer to the actual delay which is desirable. In a
similar way, Rdt and Rpt provide a measure of the fraction
of time the stimulation is OFF with an ideal predictor (which
would predict the exact time when tremor re-appeared) and the
one designed. Rdt values can be used to assess if a particular
patient is well suited for this type of application. If Rdt is very
low, i.e., if the stimulation is OFF for just 10% of the total
time then it is better just to have DBS-ON continuously. R∗pt
provides a measure of the fraction of total trial time that the
stimulation would be OFF if the predictor worked only for the
optimum stimulation duration, Ton∗ . The goal of our tremor
predictor is to maximize Rpd as well as R∗pt since with a higher
R∗pt , the patient will have a high percentage of ‘tremor-free’
DBS-OFF interval. Ton∗ can be chosen as outlined in [8].
FA =
χ 2 = N × mcc2
(20)
where the χ statistic can be used to calculate the p-value in
order to accept/reject the null hypothesis that the predictions
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(a)
(b)
Figure 5. (i) Acceleration, (ii) smoothed extensor sEMG recorded from (a) a PD patient at rest. Sample entropy (bottom) calculated from
the smoothed sEMG was used for predicting tremor. The vertical solid lines show instants of DBS-OFF (31 s) and tremor appearance (62 s),
dashed line shows instant (60.75 s) when tremor was predicted using (iii) sample entropy. (b) an ET patient while holding a posture
(sEMG)
(sEMG)
(initiated at 22 s). DBS was OFF at 25 s and there was no tremor during the entire trial of 50 s. (iii) Pmax
(in blue) and Fmax
(in
(sEMG)
while the one in dashed line shows the
black)were used to predict tremor. The red horizontal solid lines show the thresholds for Fmax
(sEMG)
(sEMG)
(sEMG)
exceeds threshold and Fmax
. The vertical dashed line shows the instant (37.5 s) when tremor was predicted (Pmax
threshold for Pmax
falls in the prediction range simultaneously) due to an artifact circled in blue.
which was recorded from PD1 in R, with a total recording
duration, Ttot = 80s. DBS was OFF at 31 s and tremor reappeared at 62 s which is also seen from the acc data (indicated
by a solid black line). The sample entropy, SpEn calculated
from the smoothed sEMG was the parameter that met the
tremor prediction criterion the earliest (at 60.75 s) amongst
the others considered as listed in table 5. This is shown in
figure 5(a) (iii). The tremor prediction time is shown with
3. Results
The parameters and corresponding thresholds used in the
prediction algorithm were determined for each patient based on
training trial data as described in section 2.5.3. The prediction
parameters used for each patient are listed in table 5 and
the threshold values are listed in tables A1–A3. The data in
figure 5(a) shows (i) the acc signal and (ii) smoothed sEMG
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(a)
(b)
Figure 6. (a)(i)acceleration and (ii) smoothed extensor sEMG (middle) recorded from a PD patient while performing a voluntary movement
initiated around 33 s. (iii) Recurrence rate calculated from the smoothed sEMG was used for predicting tremor. The vertical solid lines show
instants of DBS-OFF (41 s) and tremor appearance (56 s), dashed line shows instant (50 s) when tremor was predicted using recurrence rate.
(b) an enlarged view of raw extensor sEMG (left), smoothed extensor sEMG (right) during the trial as in (a), (i) burst without tremor, (ii)
tremor was predicted (iii) after tremor was detected visually. (a) PD Action trial(A). (b) PD Action burst.
black dotted line. Figure 5(a) thus represents a TP trial since
the tremor is predicted just a few seconds before it actually
re-appears.
The trial shown in figure 5(b) was recorded from ET4 in
P and shows a case of a FP, that is the algorithm predicted
a tremor when there was actually no tremor for the entire
recording interval (Ttot = 50 s). DBS was OFF at 25 s and
the patient initiated a posture at 22 s. From the (i) acc and
(ii) smoothed sEMG signal in figure 5(b), it can be seen that
there were some movements during the 22–34 s interval after
the posture initiation, but no tremor after that. The prediction
(sEMG)
(sEMG)
parameters shown in (iii), Pmax
(in blue) and Fmax
(in
(sEMG)
black) met the prediction criterion at 37.5 s (Fmax
∈ ( fl , fh )
(sEMG)
> fp at the same time instant ), thus resulting in
and Pmax
a false alarm. The y-axis of the smoothed sEMG has been
enlarged to show the small artifact after 35 s which probably
caused a false alarm at 37.5 s.
A trial recorded from PD1 in A is shown in figure 6(a) and
consists of (i) acc , (ii) smoothed sEMG and (iii) the prediction
parameter, recurrence rate (R) calculated from the smoothed
sEMG. DBS was OFF at 41 s and tremor re-appeared visually
at 56 s (indicated by a solid black line). Since figure 6(a)
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Table 6. Statistical analysis of the prediction outcomes, A, S, FA in %. The first row for each patient corresponds to the metrics calculated
on all experimental trials (all) while the second row shows the same calculated for testing trials (test) which were not used for training. Note:
NC means not calculated; mcc could not be calculated for PD3 (NA) due to an indeterminate form (TN = FN = 0); a p-value was calculated
only when N > 10; FA for PD4 and for overall PD are not same because although FA was not calculated for PD(1–3), PD(1–2) had a few
NTD trials which were included in the calculation of overall FA.
Patient
Trial
N,NTD
TP,TN,FP,FN
A
S
FA
mcc, p-value
PD1
All
test
All
test
All
test
All
test
All
test
All
test
All
test
All
test
All
test
All
test
16,2
9,1
26,4
16,2
17,0
10,0
32,11
20,7
91,17
55,10
15,3
9,2
30,14
18,7
16,6
10,5
30,20
18,12
91,43
55,26
14,1,1,0
7,1,1,0
16,3,7,0
10,2,4,0
15,0,2,0
9,0,1,0
16,8,8,0
10,5,5,0
61,12,18,0
36,8,11,0
10,2,3,0
5,2,2,0
13,14,3,0
9,7,2,0
10,5,1,0
5,4,1,0
7,17,6,0
4,11,3,0
40,38,13,0
23,24,8,0
93.8
88.9
73.1
75.0
88.2
90.0
75.0
75.0
80.2
80.0
80.0
77.8
90.0
88.9
87.5
90.0
80.0
83.3
85.7
85.5
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
NC
NC
NC
NC
NC
NC
27.3
28.6
29.4
20.0
NC
NC
0
0
16.7
20
15
8.3
11.6
7.7
0.68, 0.006
0.66,NC
0.46, 0.02
0.49,0.05
NA
NA
0.53, 0.003
0.58, 0.01
0.56, 10−10
0.57, 10−4
0.55, 0.03
0.6, NC
0.82, 0.0001
0.8, 0.0007
0.87, 0.0005
0.82, NC
0.63, 0.0005
0.67, 0.005
0.71, 0
0.75, 10−8
PD2
PD3
PD4
All PD
ET1
ET2
ET3
ET4
All ET
(ii) lacks the resolution to show each sEMG action burst,
portions of it are enlarged and shown in figure 6(b). The
sEMG (left) and corresponding smoothed signal (right) during
different intervals of the trial as in figure 6(a) (ii) are shown
in figure 6(b): (i) when there was no tremor, (ii) when tremor
was predicted and (iii) after tremor was detected (bottom). It
can be seen that the burst with tremor (iii) and the one when
tremor is predicted (ii) has a more regular structure than the
one without it (i) which is captured by a greater increase in R.
A, S, FA in equations (16), (17), (18), mcc in equation
(19) and the corresponding p-values were calculated for all
experimental trials and for all testing trials corresponding to
each patient and are listed in table 6. An overall A, S, FA and
mcc based on all experimental and testing trials in PD and
in ET are also included in table 6. The ratios, Rpd , Rpt , Rdt
in equations (21), (23), (22); the optimum DBS-ON duration
Ton∗ and the corresponding R∗pt were also calculated for each
patient based on all experimental trials which are listed in
table 7. It also lists for each patient, the average of the actual
delay in tremor re-appearance (ttr − toff ) and the predicted
delay (tpr − toff ), over all experimental trials with the optimum
DBS-ON duration.
Table 7. Optimum DBS-ON time, average DBS-OFF time without
tremor (in seconds) and ratios expressed in % for all experimental
trials.
Patient
Rpd
Rpt , Rdt
Ton∗
R∗pt
Average
(ttr − toff )
Average
(tpr − toff )
PD1
PD2
PD3a
PD4
ET1
ET2
ET3
ET4
77.6
67.0
68.8
80.7
80.2
89.8
79.1
88.7
28.6, 34.1
28.5, 37.7
11.5, 15.9
34.3, 39.3
35.2, 40.4
36.8, 39.3
32.8, 38.1
48.3, 51.3
30–32
20–37
42–55
22–37
28–38
20–40
30–42
17–30
34.1
31
11.5
42.4
37.4
39.1
32.8
45.7
21.25
20.64
9.56
24.11
24.03
18.67
22.18
27.49
16.06
13.02
6.96
20.03
19.0
15.53
17.53
24.22
a
Indicates that Ton∗ is the range of all DBS-ON intervals recorded
and not necessarily the optimum one.
Following is a brief discussion about the algorithm parameters
and prediction results.
4.1. Algorithm parameters
The set of parameters considered for predicting tremor consists
of some basic spectral measures such as mean frequency,
power at maximum frequency and the power in a particular
frequency band as well as a few nonlinear measures such as a
couple of entropy measures and a recurrence measure. The FFT
based spectral measures are commonly used parameters [31]
for sEMG analysis. Classical spectral characteristics of sEMG
have some diagnostic value for quantification of motor unit
synchronization [32]. However, sEMG signals are nonlinear in
nature and hence nonlinear time-series analyses of sEMG can
potentially provide additional information on the underlying
motor strategies [33]. Wavelet entropy has been widely used
for analyzing electroencephalogram (EEG) signals to measure
4. Discussion
The aim of this study was to design an algorithm for predicting
pathological tremor in patients with PD and ET, using noninvasively measured sEMG and acc signals from the tremor
affected limbs. The designed algorithm achieves a 100%
sensitivity for all trials considered, along with an overall
accuracy of 85.7% for all ET trials and 80.2% for all PD
trials. A Pearson’s chi-square test shows that the prediction
results significantly differ from a random prediction outcome.
14
J. Neural Eng. 10 (2013) 036019
I Basu et al
degree of similarity between different segments of the signal
[24] and for detecting different events such as seizures in
epileptic patients [34]. It has been shown that tremor is
characterized by an increased regularity in the corresponding
sEMG signal as compared to sEMG without tremor which
can be captured by the approximate entropy measure [20]. It
has also been used for similar analysis of EEG signals [35]
and heart rate signals [26]. Sample entropy was developed
to overcome some shortcomings of the approximate entropy
statistics such as bias, relative inconsistency and dependence
on the sample length [36]. Based on these two types of entropy
measures, we used wavelet entropy to capture information
relating to power shifts in different frequency bands and
sample entropy to quantify the regularity and complexity of
a time series signal [36]. These two measures are however
not directly comparable. RQA has been extensively used
for analysis of sEMG for detecting hidden characteristics
that cannot be detected by linear analysis [31, 33]. Different
variables can be extracted from a recurrence plot [37] which
has been shown to correlate with synchronization in the
signal and is more sensitive to changes in the degree of
synchronization than linear variables such as mean/median
frequency [33].
From table 5, it can be seen that the sample entropy
along with the power in the (8–16 Hz) wavelet band are the
parameters that have been used for six out of eight patients
which indicates that it has high predictive information. The
recurrence rate and mean frequency are the only parameters
used for predicting action tremor in all ET patients. Wavelet
entropy (Hwt ) is useful only in PD patients but not for ET.
This might be due to the fact that Parkinsonian tremor has a
more well defined and narrow range of frequency than in ET.
Although acc is used in the state classification for ET, its not
used in the actual prediction algorithm. acc was most useful
for predicting rest tremor in PD. However, it is not useful in
predicting action tremor. Hence a set of parameters from both
these signals were essential to predict all three types of tremor,
which could not have been achieved by using either of them
alone.
Since PD and ET have very different pathologies with
different types of tremor, it can be expected that we would
require different parameters to predict tremor in the two
disorders. The variance of parameters within the PD group
may be higher than that in the ET group due to the fact that
PD patients do not all exhibit the same symptoms. Some have
more tremor while others have more rigidity than tremor. In
ET patients, some have more tremor during holding a posture
while some have more tremor while performing an active
movement. All these factors could have contributed to the
different set of parameters required to predict tremor.
A and S are quite high, which is desirable. PD4 was the only
PD patient who had long delays in tremor with 11/32 NTD
trials. The lower A and relatively high FA is because of the
fact that the algorithm predicted a tremor either in the NTD
cases or predicted a tremor too early for the TD cases. PD2 had
moderate tremor amplitude and all the FP’s except one are due
to early prediction in the TD cases. ET3 and ET4 had almost no
postural tremor (in state P) but had tremor while performing an
active movement (state A). ET2 had very low amplitude tremor
on the right hand. The FA could not be calculated for every
patient because some of them had very few or no NTD trials.
Hence an overall FA value was also calculated for ET and PD by
considering all ET and PD trials respectively. It should be noted
that for this application, we aim to achieve S as close to 100%
as possible. Hence, for patients who have relatively higher
delays in tremor re-appearance and/or lower tremor amplitude,
the algorithm predicts early tremor events for some of the trials
resulting in a higher value of FA. The values of A, S, FA for
each patient are not very different when considering all the
experimental trials or just the testing trials. Some of the metrics
actually show better values when considering only the testing
trials. This is because while training, we tried to maximize the
number of training trials producing desired output using the
same threshold values. This however does not mean that all
the training trials actually had 100% accuracy.
4.3. Statistics based on mcc
The mcc value was calculated for each patient, except PD3 due
to an indeterminate form produced by TN = FN = 0. For all the
seven patients, the mcc value was close to or greater than 0.5
and the overall mcc for both PD and ET were above 0.5. This
indicates 50% or higher correlation between predicted and
actual classification for each patient (PD/ET) and for overall
PD and ET trials. The mcc was further used to calculate the χ 2
test statistics according to equation (20) in order to determine
the corresponding p-value. With all experimental trials, each
p-value (both individual and overall) was less than α = 5%,
which indicates that the null hypothesis that the prediction
is completely random can be rejected. When considering the
testing trials alone, the χ 2 test statistics could not be calculated
for all patients since the number of trials for PD1, ET1 and ET3
were not sufficient for a reliable estimate [30]. In general, for
all patients the p-value increases when considering the testing
trials alone due to a reduction in the sample size.
4.4. Overall performance
The overall S for both ET and PD is 100% which means that
for all TD trials in ET and PD, the predictor does not miss
any tremor event during the DBS-OFF interval. The overall A
for ET is 85.7% while for PD is 80.2%, which indicates that
in 85.7% of all ET trials and in 80.2% of all PD trials, the
algorithm correctly predicts tremor. Correct tremor prediction
means that, in the TD trials, tremor is predicted not too early
while in the NTD trials, tremor is not predicted. The overall
FA for ET is 11.6% and for PD is 29.4%, which means that
in 11.6% of the ET NTD trials and in 29.4% of the PD NTD
trials, the algorithm predicts tremor.
4.2. Prediction performance based on A, S, FA
The predictor does not miss any tremor event (S = 100%)
and it achieves a high accuracy (A > 80) for six/eight patients
as shown in table 6. Out of the eight patients, PD1 and PD3
had high tremor amplitude with a lower value of Rdt , as is also
reflected from the low values of NTD. For both of them, the
15
J. Neural Eng. 10 (2013) 036019
I Basu et al
Electric jolts that a patient might experience during
switching DBS ON can be minimized by using a gradual
ramping of the current (which is generally used) and is of lesser
concern to patients with bipolar stimulation than monopolar.
The performance for ET is in general better than that for
PD with higher overall A and lower FA because of a preceding
classification step that allowed to choose different parameters
for each state. A classification in PD is more challenging due
to the fact that a change in power in the sEMG/acc from the
rest state could either be due to tremor or movement while for
ET it is certain that there is no tremor during rest and hence an
increase in power is certainly due to some movement initiation
which might be accompanied by tremor.
5. Conclusion
In this paper, we have designed an algorithm for predicting
pathological tremor by using a set of frequency related
and entropy type parameters calculated from non-invasively
measured sEMG and acc signals. This can be used to
successfully predict a tremor event during a DBS-OFF interval,
before it occurs. Hence, this method can potentially be used for
closed-loop on-demand ON–OFF DBS paradigm which can be
added on to the existing system as in figure 1. The recording
and processing of data and transmitting the control ON/OFF
information to the pulse generator can be done within the
sEMG/acc sensor which will be powered by a separate battery
incorporated in the sensor electrodes on patient’s muscles and
will not affect the IPG battery. The battery in the sensor and
switching assembly can be easily replaced and do not require
any surgical intervention. Moreover, they do not contribute to
current injected in the brain. Thus, the proposed system will
achieve lower current injection and reduced frequency of IPG
replacement.
4.5. Practical considerations
If the tremor prediction algorithm were implemented in
practice, this would definitely be patient specific in terms
of the degree of benefit that it would provide. It might not
be beneficial to certain patients with severe tremor and short
delays in tremor, such as PD3, who had the lowest value of Rdt
as in table 7. For all other patients, R∗pt > 30% which means
that with the optimal stimulation duration, Ton∗ , the proposed
adaptive ON–OFF DBS controller achieves a ‘tremor free
DBS-OFF period’ that is greater than 30% of the total ON–
OFF duration as shown by the R∗pt values in table 7. That is,
with such a system, the battery life will be extended by a
factor of (tpr − toff )/Ton∗ = R∗pt /(1 − R∗pt ). Although PD4 has
the highest FA, the average prediction delay is 20 s for an
ON-DBS period of 22–37 s and R∗pt = 0.42. This means that
if this patient had a battery life of 5 years with the stimulator
ON continuously, then with the new system, the battery would
last for (1 + 0.72) × 5 = 8.6 years.
The tremor prediction algorithm requires determining an
optimal set of parameters and setting their thresholds for each
patient which requires some training and manual intervention.
This can be easily overcome by using similar parameter set as
inputs to a neural network [38] which can train itself at regular
intervals of time as the thresholds might change over longer
time periods due to disease progression. Initial prediction
results using such a neural network on a subset of the PD
data set has been published [39].
We acknowledge that this type of DBS controller takes
into account only the tremor symptom while PD patients also
suffer from rigidity and slowness of movement. However,
for most PD patients who have tremor, it is the symptom
that re-appears the earliest after the DBS is switched OFF
[17]. In many PD patients speech performance is worsened
during simulation at the STN [40]. For such cases, it is
reasonable to assume that such worsening will not occur
during DBS-OFF periods under our proposed control. For
ET patients, this type of DBS controller may have additional
benefit beyond just extending battery life, as it has been
shown that for some ET patients the stimulation benefits
decrease over time [41]. It was also seen that for some such
ET patients, restarting the stimulation after its temporary
discontinuation resensitized them to stimulation [42]. Hence
lesser and discontinuous current injection might actually help
in prolonging the therapeutic effects of DBS in ET.
Acknowledgments
The authors would like to thank Dr Fabian Jude David, Dr
Julie Robichaud and Dr Cynthia Poon from NCML for helping
with the equipment setup and data collection. The work of
Ishita Basu, Pitamber Shukla, Daniela Tuninetti and Daniel
Graupe was partially funded by NSF under award number
1134296. The data collection, patient payment was partially
funded by NIH under award number R56 NS 040902-11 and
the Parkinson’s Disease Foundation. Dr Leo Verhagen Metman
gratefully acknowledges the support from the Parkinson’s
Disease Foundation, educational support and consultation fees
from Medtronic, Inc.
Appendix. Thresholds on parameters for
classification and prediction
Table A1. Threshold for state classification in ET.
Patient
Threshold
ET1
ηc1 = 0.3, ηc2 = 0.6, ηc3 =
1.2, ηc4 = 0.15, ηc5 = 0.2, λ = 0.2
ηc1 = 0.06, ηc2 = 0.5, ηc3 =
3, ηc4 = 0.4, ηc5 = 0.15, λ = 0.2
ηc1 = 0.1, ηc2 = 0.6, ηc3 = 3, ηc4 =
0.07, ηc5 = 0.01, λ = 0.1
ηc1 = 0.5, ηc2 = 1.1, ηc3 =
1.6, ηc4 = 0.1, ηc5 = 0.15, λ = 0.2
ET2
ET3
ET4
16
J. Neural Eng. 10 (2013) 036019
I Basu et al
Table A2. Parameter threshold for prediction algorithm for ET.
Patient
Parameter with w1 , w2 = 1
Threshold
ET1
SpEn and P4 (w1 )
sEMG
sEMG
Pmax
and Fmax
(w1 )
R(w2 )
sEMG
Fmean
(w2 )
sEMG
sEMG
Pmax and Fmax
(w1 )
R(w2 )
SpEn and P4 (w1 )
sEMG
Fmean
(w2 )
SpEn and P4 (w1 )
R(w2 )
sEMG
Fmean
(w2 )
SpEn and P4 (w1 )
sEMG
sEMG
Pmax
and Fmax
(w1 )
R(w2)
(ηl1 , ηh1 ) = (0.25, 0.3); (ηl2 , ηh2 ) = (0.18, 0.22); ηp = 10;
fp = 22; ( fl , fh ) = (5, 10)
(ρl , ρh ) = (0.3, 0.35)
( fl , fh ) = (10, 11)
fp = 20; ( f l, f h) = (4, 10)
(ρl , ρh ) = (0.3, 0.4)
(ηl1 , ηh1 ) = (0.45, 0.5); (ηl2 , ηh2 ) = (0.1, 0.2); ηp = 10;
( fl , fh ) = (11, 12)
(ηl1 , ηh1 ) = (0.3, 0.4); (ηl2 , ηh2 ) = (0.16, 0.18); ηp = 50;
(ρl , ρh ) = (0.4, 0.48)
( fl , fh ) = (11, 12)
(ηl1 , ηh1 ) = (0.78, 0.98); (ηl2 , ηh2 ) = (0.38, 0.48); ηp = 20;
fp = 22; ( fl , fh ) = (5, 10)
(ρl , ρh ) = (0.3, 0.34)
ET2(left)
ET2(right)
ET3
ET4
Table A3. Parameter threshold for prediction algorithm for PD.
Patient
Parameter with w = 1
Threshold
PD1
SpEn and P4
SpEn and P4
(ηl1 , ηh1 ) = (0.2, 0.35); (ηl2 , ηh2 ) = (0.11, 0.2); ηp = 15;
(ηl1 , ηh1 ) = (0.35, 0.4); (ηl2 , ηh2 ) = (0.14, 0.34); ηp = 15;
(ρl , ρh ) = (0.2, 0.22)
fp1 = 28; ( fl1 , fh1 ) = (4, 10)
(ηl1 , ηh1 ) = (0.2, 0.32); (ηl2 , ηh2 ) = (0.1, 0.16); ηp = 28;
(hl , hh ) = (0.31, 0.35)
(hl , hh ) = (0.32, 0.36)
fp2 = 30; ( fl2 , fh2 ) = (4, 7)
(ηl1 , ηh1 ) = (0.25, 0.34); (ηl2 , ηh2 ) = (0.15, 0.22); ηp = 25;
(hl , hh ) = (0.275, 0.295)
fp2 = 30; ( fl2 , fh2 ) = (4, 7)
R
PD2
PD3
PD4
sEMG
sEMG
Pmax
and Fmax
SpEn and P4
Hwt
Hwt
acc
acc
and Fmax
Pmax
SpEn and P4
Hwt
acc
acc
and Fmax
Pmax
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