DECOMPOSITION OF SURFACE ELECTROMYOGRAMS: PRACTICAL EXPERIENCES A. Holobar 1,2...

DECOMPOSITION OF SURFACE ELECTROMYOGRAMS:

PRACTICAL EXPERIENCES

DECOMPOSITION OF SURFACE ELECTROMYOGRAMS:

PRACTICAL EXPERIENCESA. Holobar1,2

ales.holobar@delen.polito.it ( ales.holobar@uni-mb.si )

1 FEECS, University of Maribor, Slovenia2 LISiN, Politecnico di Torino, Italy

Laboratorio di Ingegneria del Sistema Neuromuscolare

e della Riabilitazione Motoria

Politecnico di Torino, Italy

Faculty of Electrical Engineering and Computer Science

University of Maribor, Slovenia

Copyright Ales Holobar, 2007. Some rights reserved. Content in this presentation is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This license is more fully described at:http://creativecommons.org/licenses/by-nc-sa/3.0/.

LISiNPolitecnico di Torino

Surface EMG • acquisition systems

(16, 64, 128 chs)• HD electrode arrays• stimulators• EMG simulators• information extraction

techniques

Signal & image processing

• TF & TS analysis• HOS• Cepstral analysis• BSS/ICA• MIMO, MISO

identification

SSLUniversity of Maribor

Arrays of surface electrodes

Select time instant

with high MU activity

Step 2

Convolution Kernel Compensation (CKC)in

stan

tane

ous

disc

harg

e ra

te (

Hz)

time (s)

Compensate MUAPshapes

Step 1

Blindly reconstruct

MU dischargepattern

estimator

Step 3

Filter out single MU discharge patterns

Step 4

mul

ticha

nnel

su

rfac

e E

MG

CKC decomposition: MU discharge patterns (abductor pollicis, force ramp contractions 0 % - 10 % MVC)

CKC decomposition: MU discharge patterns(Biceps Brachii, constant isometric contraction at 10 % MVC)

A. Holobar, D. Zazula. Correlation-based decomposition of surface EMG signals at low contraction forces, Medical & Biological Engineering & Computing, 2004, 42 (4), 487-495.

[pp

s]

2 4 6 8 10 12Time [s]

Ch

ann

el

(4,3

)

2 2.2 2.4 2.6

0

Time [s]

Am

plitu

de

Reconstructed MUAP trainsacquired EMG signal

sum of reconstructedMUAP trains

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-60

-40

-20

0

20

40

60

Normalized Frequency [ π rad/sample]

Po

we

r S

pe

ctr

al D

en

sity (

dB

/ ra

d/sa

mp

le)

400 800 1200 1600 2000 2400 2800 3200 3600 4000

0

0.1

0.2

Time [ms]

Am

plit

ud

e

200 400 600 800 1000 1200 1400 1600 1800 2000-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Time [ms]

Am

plit

ud

e [a

rbitr

ary

un

its]

0 10 0 10Time [ms]

0 100 10

MU

AP

am

plit

ud

e(P2

P 2

40 μV

)

0 10

Signal artefacts: line interference

1 2 3 4 5

13

12

11

10

9

8

7

6

5

4

3

2

1

Ele

ctro

de r

ows

Electrode columns

Signal artefacts: bad contact (biceps brachii, monopolar mode)

Internal array Central array External array

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Ele

ctro

de r

ows

Movement artefetcs & saturations:(external sphincter, bipolar mode, 100% MVC)

1

2

3

4

5

6

7

MU

num

ber

0 1 2 3 4 5 6

10 - MU 1

10 - MU 2

10 - MU 3

10 - MU 4

10 - MU 5

10 - MU 6

10 - MU 7

Time [s]

inst

anta

neou

s di

scha

rge

rate

[H

z]

Decomposition & ground truth(external sphincter, bipolar mode)

Similar shapes of MUAPs: MU 1

2 3

Time41

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

-2500

250

MU

AP

s am

plitu

de [

V]

5

Similar shapes of MUAPs: MU 2

2 3

Time41

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

-880

88

MU

AP

s am

plitu

de [

V]

5

Similar shapes of MUAPs: MU 1 & MU 2

Similar shapes of MUAPs and reconstruction of innervation pulse trains

2 3 4 5 6 7 8 9 10 11 12MU 1

MU 2

MU 1 & 2

Time [s]

Rec

onst

ruct

ed in

nerv

atio

n pu

lse

trai

ns

Case studies: ICA & image processing homepages

• ICA – http://www.tsi.enst.fr/icacentral

• Face recognition test databases– http://www.face-rec.org/databases/– http://vision.bc.edu/~dmartin/MidLevel/

• Middlebury stereo page:– http://cat.middlebury.edu/stereo/– test database, source codes & algorithm

benchmarking

ICA central: data collections

Face recognition test databases

Face recognition test databases

Middlebury stereo page

Middlebury stereo page

Middlebury stereo page

Acknowledgement

Progetto Lagrange

This research was supported by a Marie Curie Intra-European Fellowships within the 6th European Community Framework Programme, by CyberManS EU project, Slovenian Ministry of Higher Education, Science and Technology, Italian Ministry of Foreign Affairs, Slovenian Research Agency and Lagrange project.