This study presents a signal-independent algorithm, which detects significant events in a biosignal, withoutprevious knowledge or specific pre-processing steps. From a morphological analysis, the algorithm computesthe instants when the most significant standard deviation discontinuities occur. An iterative optimization stepis then applied.  This assures that a minimal error is achieved when modeling the signal segments (betweenthe detected instants) with a polynomial regression. The detection scale can be modified by an optional inputscale factor.   An objective algorithm performance evaluation procedure was designed,  and applied on twotypes of synthetic signals, for which the events instants were previously known.  An overall mean error of20.32 ( 16.01) samples between the detected and the real events show the high accuracy of the proposedalgorithm.  The algorithm was also applied on accelerometry and electromyography raw signals collected indifferent experimental scenarios. The fact that this approach does not require any previous knowledge and thegood level of accuracy represents a relevant contribution in events detection and biosignal analysis

Gamboa, Hugo

Marques, Carlos J.

Santos, Rui

Sañudo Corrales, Francisco de Borja

Sousa, Joana

English

idUS. Depósito de Investigación Universidad de Sevilla

BIOSIGNALS EVENTS DETECTIONA Morphological Signal-independent ApproachRui Santos1, Joana Sousa2 , Borja Sañudo3, Carlos J. Marques4;5 and Hugo Gamboa1;21Physics Department, FCT-UNL, Lisbon, Portugal2PLUX- Wireless Biosignals, S.A., Lisbon, Portugal3Department of Physical Education and Sport, University of Seville, Seville, Spain4Faculty of Human Kinetics at the Technical University of Lisbon, Lisbon, Portugal5Physical Therapy and Rehabilitation Department at the Schön Klinik Hamburg Eilbek, Hamburg, GermanyKeywords: Biosignals, Signal-processing, Events, Detection and Identification, Signal-independent.Abstract: This study presents a signal-independent algorithm, which detects significant events in a biosignal, withoutprevious knowledge or specific pre-processing steps. From a morphological analysis, the algorithm computesthe instants when the most significant standard deviation discontinuities occur. An iterative optimization stepis then applied. This assures that a minimal error is achieved when modeling the signal segments (betweenthe detected instants) with a polynomial regression. The detection scale can be modified by an optional inputscale factor. An objective algorithm performance evaluation procedure was designed, and applied on twotypes of synthetic signals, for which the events instants were previously known. An overall mean error of20.32 (16.01) samples between the detected and the real events show the high accuracy of the proposedalgorithm. The algorithm was also applied on accelerometry and electromyography raw signals collected indifferent experimental scenarios. The fact that this approach does not require any previous knowledge and thegood level of accuracy represents a relevant contribution in events detection and biosignal analysis.1 INTRODUCTIONAutomated techniques for generating, acquiring andstoring 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, inwhich 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 eventis broadly defined as the change in state of the sys-tem under study (Ciaccio et al., 1993). Biosignalsare often characterized by oscillations at specific fre-quencies and contaminated by in-band noise, whichis both periodic and random (Clifford, 2006). Identi-fying the underlying biosignal and its specific eventsamong trivial changes can become extremely difficult.The accurate onset determination of electromyo-graphic (EMG) activity is an application of eventsdetection algorithms. A comparative study regardingseveral methods for EMG signals onset detection, in-cluding those proposed by Hodges (Hodges and Bui,1996) and Bonato (Bonato et al., 1998), is reported byStaude (Staude et al., 2001). Another possible appli-cation is related to the detection of general transientevents. Abrupt changes or discontinuities encoun-tered in biosignals may be symptomatic of functionaldisorders (Sankur et al., 1996). The ability to detectthem has great importance to the medical predictionprocess, as well as in sport and rehabilitation researchfields.In the present work we’ve developed an algorithmfor biosignals events detection. Based on a morpho-logical analysis, it is able to identify time domainspecific shape parameters that can clearly distinguishthose events from the complete observed signals. Thealgorithm’s design follows a signal-independent ap-proach, requiring also no prior information or pre-processing steps, but allowing the user to control thedetail on the event detection by optionally changing aspecific scale factor.The algorithm was applied on acceleration andEMG digital raw signals. An objective performanceevaluation procedure was designed and applied onsynthetic signals, for which the events were known.385Santos R., Sousa J., Sañudo B., J. Marques C. and Gamboa H. (2012).BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 385-388DOI: 10.5220/0003772403850388Copyright c SciTePressbrought to you by COREView metadata, citation and similar papers at core.ac.ukprovided by idUS. Depósito de Investigación Universidad de Sevilla2 MATERIALS ANDACQUISITION METHODS2.1 Synthetic SignalsSynthetic signals were constructed by concatenatingsections with predefined mean and standard deviation(STD) values (referenced as zones). The mean valueswhere given by float numbers randomly sampled froma standard normal distribution and then multiplied bya 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 alsoconsidered. Minimal values for mean and STD dif-ferences between successive zones were imposed andassigned to the m d and sd d variables, respectively.Two different types of synthetic signals were definedaccording to the parameters in Table 1.Table 1: Synthetic signals parameters.Synthetic signal Type I Type IIm 8 0.05s 0.1 1m d 5 0.1sd d 0.05 0.5Number of events 10 52.2 Data AcquisitionThe 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 jumpsfrom a 40 cm elevated platform to the ground levelwere executed. The accelerometer was placed nextto the knee (at the tibial condyle level) and orien-tated so its x axis was pointing upward. Only thesignal acquired from the z axis (the acceleration in theMedial-Lateral axis) was considered. The testing pro-tocol that was followed to obtain EMG signals aimedat accessing the performance of an emergency brakein a car simulator (Marques et al., 2011). From theset of signals acquired in the context of that study weconsider the EMG records from the Tibialis anteriormuscle during the foot transition from the acceleratorto the brake pedal.3 EVENTS DETECTIONALGORITHM3.1 Mathematical FormalismFor a given signal defined as a time series, x(t), witht = 1;2; :::;L, a set of regions is created by slicing thesignal. Considering E as the total number of events,a general event slicing signal regions is denoted byei; i = 1;2; :::;E. The complete modeled signal is ex-pressed as defined in equation 1, for which further no-tation is described below:x̂(t) =E 1åi=0Qtei+1  ei  eiM(t  ei;Ai;e) (1)Q(t) is an unit pulse function expressed by:Q(t) =1 i f 0 t  10 otherwise (2)M(t;A;e) is a polynomial regression model de-fined by:M(t;A;e) = A(t)+ e (3)where A is the matrix with n polynomial parametersfor a given signal region A(t) = a0+a1t+ ::::+antnand e is an error term that is assumed to follow a nor-mal distribution e=N(µ;s2)with µ= 0. Increasingthe order of M makes the polynomial regression tobetter model the signal, decreasing the Mean SquaredError (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:x̂i(t) = x̂(t)jt2[ei;ei+1] (4)3.2 Implementation3.2.1 Events DetectionThe get_events algorithm is the base function toidentify the signal events (Figure 1). Beyond the sig-nal, it can receive other parameters, by input, in orderto run auxiliary functions responsible for each of theprocessing steps described below.From a raw signal, the get_slices_std functiondivides 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 calculatedand low-pass filtered by applying a smoothing fil-ter, in which the number of points considered intothe moving average window depends on the lengthof the sequence multiplied by the smooth f actorBIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing386Figure 1: get_events algorithm flowchart diagram.value (Figure 1). From the filtered sequence, thesignal notable points are computed from the lo-cal maximums/minimums found above/below a spe-cific threshold. The former is defined multiplyingthe absolute sequence maximum/minimum by thepeaks f actor input parameter (Figure 1).The adjust_peaks function is then applied. Foreach peak this function considers the signal slice be-ginning in each notable point and then applies theget_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 thepoint that maximizes the difference on the computedSTD sequence. For minimums the procedure is analo-gous. This processing step ensures a more accurateevents 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 signalregions between those events.3.2.2 OptimizationThe optimization processing step is based onan iterative change into the input parametersof the get_events algorithm (slices length,smooth f actor and peaks f actor). The respectiveranges depend on a scale factor, defined with base onthe signal’s length. It can alternatively be an input ofthe optimization algorithm, allowing to increase ordecrease the level of detail (the number of detectedevents). For each set of parameters the get_eventsalgorithm is applied. Either the solutions that havethe minimum total MSE, considering all the signalsegments (x̂i(t) in equation 4) modeled as 1st orderpolynomials, or the maximum total standard devia-tion differences between those segments are selectedin the same step. From those, the one for whichthe number of events is maximal is selected as theoptimal solution.4 RESULTS AND DISCUSSION4.1 Algorithm Performance EvaluationThe developed algorithm was applied on a total of 25Type I and 25 Type II synthetic signals, for whichexamples of graphic representations, including markson the detected events, are given in Figure 2. The de-fault scale factor was used. For each synthetic signalthe detection mean error was computed by evaluat-ing the mean of the differences between the detectedevents and the closer real event. An overall mean er-ror of 20.32 (16.01) samples show the high accuracyof 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 BiosignalsThe default mode algorithm application to knee ac-celeration signals (Figure 3a) ) marks the drop jumpevent by selecting mostly the landing phase record,which is characterized for the greater instability andamplitude within the signal. A more accurate de-scription of the acceleration pattern is achieved witha proper scale factor. The results presented in Figure3b), with a scale factor of 0.02, allow to distinguishthe knee acceleration while preparing the jump fromthe platform, the flying phase (with a slight and longerBIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach387Figure 3: Acceleration signal results considering: a) defaultscale factor and b) a scale factor of 0.02.acceleration instability) and the landing phase, wherethe instability increases significantly for a short timeperiod. The time spent recovering the knee stabilitycan also be estimated by accessing the posterior sig-nal segments. When applied to EMG signals, the de-fault mode events detection allowed a clear distinctionbetween activation and rest phases with an acute on-set and offset detection. Being able to detect multipleevents and requiring no EMG specific pre-processingsteps, this algorithm presents advantages when com-pared with the standard onset detection techniques(Staude et al., 2001).5 CONCLUSIONS AND FUTUREWORKThe proposed algorithm performs an efficient eventsdetection within a signal. Its versatile design allowsthe application in different signals, without previousknowledge on their statistical characteristics and theadjustment of a scale factor to achieve different detaillevels in specific applications. The added accuracyand objectivity of this algorithm when compared withthe 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 andevaluate its performance when compared with signalspecific processing techniques. Its integration into areal-time processing tool is already under develop-ment. Preliminary results point out the application’sability to detect the events in real-time, without a sig-nificant loss of accuracy.ACKNOWLEDGEMENTSThis work was partially supported by NationalStrategic Reference Framework (NSRF-QREN) un-der projects ”LUL” and ”Affective Mouse”, and Se-venth Framework Programme (FP7) program underproject ICT4Depression, whose support the authorsgratefully acknowledge.REFERENCESBonato, P., D’Alessio, T., and Knaflitz, M. (1998). A sta-tistical method for the measurement of muscle activa-tion intervals from surface myoelectric signal duringgait. Biomedical Engineering, IEEE Transactions on,45(3):287–299.Ciaccio, E., Dunn, S., and Akay, M. (1993). Biosignalpattern 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 filteringand segmentation of biosignals. Journal of BiologicalSystems, 14(2):169–184.Hodges, P. and Bui, B. (1996). A comparison ofcomputer-based methods for the determination ofonset 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., andCabri, J. (2011). Muscle activation thresholds beforeand after total knee arthoplasty - protocol of a ran-domized comparison of minimally invasive vs. stan-dard approach. In Proceedings of the InternationalConference on Bio-inspired Systems and Signal Pro-cessing.PLUX (2007). PLUX - Wireless Biosignals, S.A. [online]Available at: http://plux.info/ [Accessed 5 September2011].Sankur, B., Güler, 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 Processing388

Biosignals events detection.  A Morphological Signal independent Approach

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Biosignals events detection. A Morphological Signal independent Approach

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