BIOSIGNALS EVENTS DETECTION

A Morphological Signal-independent Approach

Rui Santos

, Joana Sousa

, Borja Sa

nudo

, Carlos J. Marques

4,5

and Hugo Gamboa

1,2

Physics Department, FCT-UNL, Lisbon, Portugal

PLUX- Wireless Biosignals, S.A., Lisbon, Portugal

Department of Physical Education and Sport, University of Seville, Seville, Spain

Faculty of Human Kinetics at the Technical University of Lisbon, Lisbon, Portugal

Physical Therapy and Rehabilitation Department at the Sch ¨on Klinik Hamburg Eilbek, Hamburg, Germany

Keywords:

Biosignals, Signal-processing, Events, Detection and Identiﬁcation, Signal-independent.

Abstract:

This study presents a signal-independent algorithm, which detects signiﬁcant events in a biosignal, without

previous knowledge or speciﬁc pre-processing steps. From a morphological analysis, the algorithm computes

the instants when the most signiﬁcant standard deviation discontinuities occur. An iterative optimization step

is then applied. This assures that a minimal error is achieved when modeling the signal segments (between

the detected instants) with a polynomial regression. The detection scale can be modiﬁed by an optional input

scale factor. An objective algorithm performance evaluation procedure was designed, and applied on two

types of synthetic signals, for which the events instants were previously known. An overall mean error of

20.32 (±16.01) samples between the detected and the real events show the high accuracy of the proposed

algorithm. The algorithm was also applied on accelerometry and electromyography raw signals collected in

different experimental scenarios. The fact that this approach does not require any previous knowledge and the

good level of accuracy represents a relevant contribution in events detection and biosignal analysis.

1 INTRODUCTION

Automated techniques for generating, acquiring and

storing data from scientiﬁc measurements have be-

come increasingly precise and powerful. However,

there is still a practical need to improve tools for sig-

nal pattern recognition and interpretation systems, in

which the detection of speciﬁc events and the auto-

matic signal segmentation must be one of the ﬁrst pro-

cessing procedures (Ciaccio et al., 1993). An event

is broadly deﬁned as the change in state of the sys-

tem under study (Ciaccio et al., 1993). Biosignals

are often characterized by oscillations at speciﬁc fre-

quencies and contaminated by in-band noise, which

is both periodic and random (Clifford, 2006). Identi-

fying the underlying biosignal and its speciﬁc events

among trivial changes can become extremely difﬁcult.

The accurate onset determination of electromyo-

graphic (EMG) activity is an application of events

detection algorithms. A comparative study regarding

several methods for EMG signals onset detection, in-

cluding those proposed by Hodges (Hodges and Bui,

1996) and Bonato (Bonato et al., 1998), is reported by

Staude (Staude et al., 2001). Another possible appli-

cation is related to the detection of general transient

events. Abrupt changes or discontinuities encoun-

tered in biosignals may be symptomatic of functional

disorders (Sankur et al., 1996). The ability to detect

them has great importance to the medical prediction

process, as well as in sport and rehabilitation research

ﬁelds.

In the present work we’ve developed an algorithm

for biosignals events detection. Based on a morpho-

logical analysis, it is able to identify time domain

speciﬁc shape parameters that can clearly distinguish

those events from the complete observed signals. The

algorithm’s design follows a signal-independent ap-

proach, requiring also no prior information or pre-

processing steps, but allowing the user to control the

detail on the event detection by optionally changing a

speciﬁc scale factor.

The algorithm was applied on acceleration and

EMG digital raw signals. An objective performance

evaluation procedure was designed and applied on

synthetic signals, for which the events were known.

385

Santos R., Sousa J., Sañudo B., J. Marques C. and Gamboa H..

BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach.

DOI: 10.5220/0003772403850388

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 385-388

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 MATERIALS AND

ACQUISITION METHODS

2.1 Synthetic Signals

Synthetic signals were constructed by concatenating

sections with predeﬁned mean and standard deviation

(STD) values (referenced as zones). The mean values

where given by ﬂoat numbers randomly sampled from

a standard normal distribution and then multiplied by

a factor m. The STD values were obtained by multi-

plying a random number sampled from a uniform dis-

tribution, within the interval [0, 1] by a factor s. Be-

tween those zones transition events with known ran-

domly selected starting and ending points were also

considered. Minimal values for mean and STD dif-

ferences between successive zones were imposed and

assigned to the m d and sd d variables, respectively.

Two different types of synthetic signals were deﬁned

according to the parameters in Table 1.

Table 1: Synthetic signals parameters.

Synthetic signal Type I Type II

m 8 0.05

s 0.1 1

m d 5 0.1

sd d 0.05 0.5

Number of events 10 5

2.2 Data Acquisition

The EMG and acceleration signals were acquired, re-

spectively, using surface bipolar EMG sensors (emg-

PLUX) and triaxial accelerometers (xyzPLUX), con-

nected to a bioPLUX research unit (PLUX, 2007).

Signals were sampled at frequency of 1000 Hz.

For acquiring acceleration signals drop jumps

from a 40 cm elevated platform to the ground level

were executed. The accelerometer was placed next

to the knee (at the tibial condyle level) and orien-

tated so its x axis was pointing upward. Only the

signal acquired from the z axis (the acceleration in the

Medial-Lateral axis) was considered. The testing pro-

tocol that was followed to obtain EMG signals aimed

at accessing the performance of an emergency brake

in a car simulator (Marques et al., 2011). From the

set of signals acquired in the context of that study we

consider the EMG records from the Tibialis anterior

muscle during the foot transition from the accelerator

to the brake pedal.

3 EVENTS DETECTION

ALGORITHM

3.1 Mathematical Formalism

For a given signal deﬁned as a time series, x(t), with

t = 1, 2, ..., L, a set of regions is created by slicing the

signal. Considering E as the total number of events,

a general event slicing signal regions is denoted by

, i = 1, 2, ..., E. The complete modeled signal is ex-

pressed as deﬁned in equation 1, for which further no-

tation is described below:

ˆx(t) =

E−1

∑

i=0



i+1

− e



M(t − e

, A

, ε) (1)

Q(t) is an unit pulse function expressed by:

Q(t) =



1 i f 0 ≤ t ≤ 1

0 otherwise

(2)

M(t, A, ε) is a polynomial regression model de-

ﬁned by:

M(t, A, ε) = A(t) + ε (3)

where A is the matrix with n polynomial parameters

for a given signal region



A(t) = a

t +.... +a



and ε is an error term that is assumed to follow a nor-

mal distribution



ε = N(µ, σ

)



with µ = 0. Increasing

the order of M makes the polynomial regression to

better model the signal, decreasing the Mean Squared

Error (MSE), but also renders this algorithm less sen-

sitive to abrupt changes within that signal.

After modeling the signal as described above,

each one of the E − 1 signal segments can be des-

cribed as:

ˆx

(t) = ˆx(t)|

t∈[e

i+1

]

(4)

3.2 Implementation

3.2.1 Events Detection

The get_events algorithm is the base function to

identify the signal events (Figure 1). Beyond the sig-

nal, it can receive other parameters, by input, in order

to run auxiliary functions responsible for each of the

processing steps described below.

From a raw signal, the get_slices_std function

divides the signal into slices with a deﬁned length

(slices length). This function returns an array con-

taining the sequence of the successive slice’s STD.

The STD sequence ﬁrst derivative is then calculated

and low-pass ﬁltered by applying a smoothing ﬁl-

ter, in which the number of points considered into

the moving average window depends on the length

of the sequence multiplied by the smooth f actor

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

386

Figure 1: get_events algorithm ﬂowchart diagram.

value (Figure 1). From the ﬁltered sequence, the

signal notable points are computed from the lo-

cal maximums/minimums found above/below a spe-

ciﬁc threshold. The former is deﬁned multiplying

the absolute sequence maximum/minimum by the

peaks f actor input parameter (Figure 1).

The adjust_peaks function is then applied. For

each peak this function considers the signal slice be-

ginning in each notable point and then applies the

get_slices_std function on that signal segment

(with a ﬁxed slices length of 5 samples). If a maxi-

mum peak is considered, it is then replaced by the

point that maximizes the difference on the computed

STD sequence. For minimums the procedure is analo-

gous. This processing step ensures a more accurate

events detection, with a minimum error of 5 samples.

At the end, the get_events algorithm returns the de-

tected events and an array with the successive signal

regions between those events.

3.2.2 Optimization

The optimization processing step is based on

an iterative change into the input parameters

of the get_events algorithm (slices

length,

smooth f actor and peaks f actor). The respective

ranges depend on a scale factor, deﬁned with base on

the signal’s length. It can alternatively be an input of

the optimization algorithm, allowing to increase or

decrease the level of detail (the number of detected

events). For each set of parameters the get_events

algorithm is applied. Either the solutions that have

the minimum total MSE, considering all the signal

segments ( ˆx

(t) in equation 4) modeled as 1st order

polynomials, or the maximum total standard devia-

tion differences between those segments are selected

in the same step. From those, the one for which

the number of events is maximal is selected as the

optimal solution.

4 RESULTS AND DISCUSSION

4.1 Algorithm Performance Evaluation

The developed algorithm was applied on a total of 25

Type I and 25 Type II synthetic signals, for which

examples of graphic representations, including marks

on the detected events, are given in Figure 2. The de-

fault scale factor was used. For each synthetic signal

the detection mean error was computed by evaluat-

ing the mean of the differences between the detected

events and the closer real event. An overall mean er-

ror of 20.32 (±16.01) samples show the high accuracy

of the proposed algorithm.

Figure 2: Synthetic signals results: a) Type I and b) Type II.

The detected events are represented as vertical red marks.

4.2 Application to Biosignals

The default mode algorithm application to knee ac-

celeration signals (Figure 3a) ) marks the drop jump

event by selecting mostly the landing phase record,

which is characterized for the greater instability and

amplitude within the signal. A more accurate de-

scription of the acceleration pattern is achieved with

a proper scale factor. The results presented in Figure

3b), with a scale factor of 0.02, allow to distinguish

the knee acceleration while preparing the jump from

the platform, the ﬂying phase (with a slight and longer

BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach

387

Figure 3: Acceleration signal results considering: a) default

scale factor and b) a scale factor of 0.02.

acceleration instability) and the landing phase, where

the instability increases signiﬁcantly for a short time

period. The time spent recovering the knee stability

can also be estimated by accessing the posterior sig-

nal segments. When applied to EMG signals, the de-

fault mode events detection allowed a clear distinction

between activation and rest phases with an acute on-

set and offset detection. Being able to detect multiple

events and requiring no EMG speciﬁc pre-processing

steps, this algorithm presents advantages when com-

pared with the standard onset detection techniques

(Staude et al., 2001).

5 CONCLUSIONS AND FUTURE

WORK

The proposed algorithm performs an efﬁcient events

detection within a signal. Its versatile design allows

the application in different signals, without previous

knowledge on their statistical characteristics and the

adjustment of a scale factor to achieve different detail

levels in speciﬁc applications. The added accuracy

and objectivity of this algorithm when compared with

the standard visual inspection also represents an ad-

vance in events detection from biosignals analysis.

In future work there is the intention of apply-

ing the algorithm to a wider range of biosignals and

evaluate its performance when compared with signal

speciﬁc processing techniques. Its integration into a

real-time processing tool is already under develop-

ment. Preliminary results point out the application’s

ability to detect the events in real-time, without a sig-

niﬁcant loss of accuracy.

ACKNOWLEDGEMENTS

This work was partially supported by National

Strategic Reference Framework (NSRF-QREN) un-

der projects ”LUL” and ”Affective Mouse”, and Se-

venth Framework Programme (FP7) program under

project ICT4Depression, whose support the authors

gratefully acknowledge.

REFERENCES

Bonato, P., D’Alessio, T., and Knaﬂitz, M. (1998). A sta-

tistical method for the measurement of muscle activa-

tion intervals from surface myoelectric signal during

gait. Biomedical Engineering, IEEE Transactions on,

45(3):287–299.

Ciaccio, E., Dunn, S., and Akay, M. (1993). Biosignal

pattern recognition and interpretation systems. En-

gineering in Medicine and Biology Magazine, IEEE,

12(3):89–95.

Clifford, G. (2006). A novel framework for signal represen-

tation and source separation: Applications to ﬁltering

and segmentation of biosignals. Journal of Biological

Systems, 14(2):169–184.

Hodges, P. and Bui, B. (1996). A comparison of

computer-based methods for the determination of

onset of muscle contraction using electromyogra-

phy. Electroencephalography and Clinical Neu-

rophysiology/Electromyography and Motor Control,

101(6):511–519.

Marques, C. J., Gamboa, H., Lampe, F., Barreiros, J., and

Cabri, J. (2011). Muscle activation thresholds before

and after total knee arthoplasty - protocol of a ran-

domized comparison of minimally invasive vs. stan-

dard approach. In Proceedings of the International

Conference on Bio-inspired Systems and Signal Pro-

cessing.

PLUX (2007). PLUX - Wireless Biosignals, S.A. [online]

Available at: http://plux.info/ [Accessed 5 September

2011].

Sankur, B., G

uler, E. C., and Kahya, Y. (1996). Multireso-

lution biological transient extraction applied to respi-

ratory crackles. Computers in biology and medicine,

26(1):25–39.

Staude, G., Flachenecker, C., Daumer, M., and Wolf,

W. (2001). Onset detection in surface electromyo-

graphic signals: a systematic comparison of meth-

ods. EURASIP Journal on Applied Signal Processing,

2001(1):67–81.

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

388