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Medical Engineering & Physics 35 (2013) 1059– 1069

Contents lists available at SciVerse ScienceDirect

Medical Engineering & Physics

jou rn al h om epage: www.elsev ier .com/ locate /medengphy

nsemble empirical mode decomposition based feature enhancementf cardio signals

rturas Janusauskas ∗, Vaidotas Marozas, Arunas Lukoseviciusiomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania

r t i c l e i n f o

rticle history:eceived 31 May 2011eceived in revised form 10 July 2012ccepted 16 October 2012

eywords:nsemble empirical mode decompositioniscrete wavelet transformCGmpedance cardiogrameature enhancement

a b s t r a c t

This paper presents an application of ensemble empirical mode decomposition method for enhancementof specific biological signal features. The application for two types of cardiological signals is presented inthis article. Detection of fiducial points is a routine task for analyzing these signals. In a clinical situation,cardiological signals are usually corrupted by artifacts and finding exact time instances of various fiducialpoints is a challenge. Filtering approach for signal to noise ratio enhancing is traditionally and widely usedin clinical practice. Methods, based on filtering, however, have serious limitations when it is necessary tofind compromise between noise suppression and preservation of signal features. The proposed methoduses ensemble empirical mode decomposition in order to suppress noise or enhance specific waves inthe signal. Performance of the method was estimated by using clinical electrocardiogram and impedancecardiogram signals with synthetic baseline-wander, power-line and added Gaussian noise. In electrocar-

diogram application, an average estimation error of QRS complex length was 2.06–4.47%, the smallestin comparison to the reference methods. In impedance cardiogram application, the proposed methodprovided the highest cross-correlation coefficient between original and de-noised signal in comparisonto reference methods. When the signal to noise ratio of the input signal was −12 dB, the method providedsignal to error ratio of 33 dB in this case. The proposed method is adaptive to template and signal itselfand thus could be applied to other non-stationary biological signals.

© 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

. Introduction

Detection of fiducial points is a usual task in biomedical signalrocessing applications. In order to achieve good detection quality,

t is necessary to have good signal to noise ratio which is a rare casen real clinical situations. Signals are often corrupted by variousrtifacts and noise [1–5].

In cardiological signals, the removal of artifacts is an importantask for further signal analysis, e.g. detection of QRS complex onsetnd offset times, T wave in electrocardiogram (ECG); detection ofharacteristic points in impedance cardiogram (ICG) and its deriva-ive (dZ/dt). Usually it is necessary to apply different preprocessingn order to enhance signal to noise ratio (SNR) and to reduce otherignal events [6,7].

Many signal processing methods are proposed for ECG sig-al processing e.g. for specific wave or overall SNR enhancement.daptive filters have shown good performance in removingarrow-band interference and baseline wander [8,9]. However,

∗ Corresponding author. Tel.: +370 37 407118; fax: +370 37 407118.E-mail addresses: artjanu@ktu.lt, arturas.janusauskas@ktu.lt (A. Janusauskas).

350-4533/$ – see front matter © 2012 IPEM. Published by Elsevier Ltd. All rights reservettp://dx.doi.org/10.1016/j.medengphy.2012.10.007

these methods usually need additional reference channels [9,10]which means that specific recording hardware is required. A num-ber of methods based on singular value decomposition, Kalmanfilters and matching pursuit take advantage of redundancy of themultiple ECG recording channels [11–14]. The signal enhance-ment and noise reduction results obtained by using these methodsare very convincing; however the application of these methodsis limited to multichannel ECG recording systems and cannot beapplied to single channel signals such as ICG. Statistical meth-ods such as neural network, independent component analysis andprincipal component analysis are generally computationally inten-sive and sensitive to the changes in data dimensionality, e.g. whennon-stationary artifacts are present [15]. Denoising and R-peakdetection methods based on wavelet transform are very populartoo since they have good time–frequency localization [16,15,9,17].In addition, discrete wavelet transform (DWT) as one of the mostwidely used and well-established method is briefly described hereand compared with the developed EEMD based algorithm. Anotherreference method is frequency selective filtering. High-pass fil-

ters are used for removal of baseline-wander and low-pass filtersfor high frequency noise such as electromyographic or power-linenoise removal. Band-pass filters are used for QRS complex or Twave enhancement in ECG. Usually ECG and noise spectra overlap

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nd performance of linear filters is limited. However, the frequencyelective linear filters are still very common in clinical equipmentnd thus were chosen as the second reference in the presentednvestigation.

In this paper we propose a new method based on the ensemblempirical mode decomposition for enhancement of specific signaleatures.

Ensemble empirical mode decomposition (EEMD) algorithm is modification of empirical mode decomposition (EMD) method18]. A number of applications for ECG and other biomedicalignal research based on EMD has been developed [19–21] dur-ng recent years. The empirical mode decomposition method issed to decompose a signal into locally narrow band compo-ents, called intrinsic mode functions (IMFs) that do not requireny apriori known basis [22,23]. This means that EMD methods completely adaptive to the signal itself. This characteristic isoth the strongest and the weakest point of this method – thetrongest because it does do not require a usage of any basic func-ions, which influence results of decomposition and induce theertain distortion to each decomposition level. The weakest sincet is hard to predict and control essential decomposition param-ters such as frequency range in each decomposition level or itsocal frequency contents. Empirical mode decomposition methodas also a specific scale mixing shortcoming, when the same IMFontains local oscillations with dramatically different frequencies22,18]. Recently the EMD method’s modification called the ensem-le empirical mode decomposition has been presented [18,24–26].his new approach allows getting a uniform reference frame inhe time–frequency plane and avoiding mode mixing. This modi-ed method is used in the proposed feature enhancement method.omparison between EMD and EEMD decomposition results is alsoiven.

The number of EMD and EEMD based applications continu-usly grows. Many applications are presented in ECG field too.he majority of the EMD and EEMD applications however aresing predefined set of scales [27,28,19]. In this way even if EMDnd EEMD are adaptive to each signal the usage of a priori pre-efined set of scales decreases adaptivity of these methods. Inontrast, our method is allowed to use different set of IMFs. Thepecific set of IMFs depends on the signal properties. Adaptations achieved by using variable templates extracted from the signaltself. In order to control which features from the signal shoulde extracted, the cross-correlation based technique is applied.ccording to this technique, the IMFs dominated by the signalomponents that have to be enhanced, are automatically includednto the output signal. In order to estimate the performance ofhe EEMD based method, it was compared to two other well-nown methods. The first reference is band-pass filtering methodommonly used in clinical practice and the second is discreteavelet transform (DWT) time–frequency decomposition basedethod.Performance of the methods was compared by applying them

o modeled and real ECG and ICG signals.This paper is organized as follows: Section 2 introduces EMD,

EMD and DWT methods and the algorithm of the proposedethod. Section 3 describes the analyzed signals. Section 4 presents

esults of the signal feature enhancement, comparisons and discus-ion. Section 5 gives concluding remarks.

. Methods

This section briefly describes EMD and EEMD methods, pro-osed signal feature enhancement algorithm, DWT and linearltering (LF) methods. A comparison between EMD and EEMDethods is also presented.

g & Physics 35 (2013) 1059– 1069

2.1. Empirical mode decomposition

Empirical mode decomposition is particularly suitable for anal-ysis of non-stationary signals. Empirical mode decompositiondecomposes signal into locally narrow band oscillating compo-nents [22]. The IMFs result from an iterative procedure consisting ofextrema identification and “sifting” described below. The observedsignal s(t) constitutes the input to sifting, and si,k(t) defines a com-ponent of the sifting process (i denotes iteration number and ksifting component); the procedure is initialized with s1,1(t) = s(t).The following steps define sifting:

1. The local minima and maxima of si,k(t) are determined.2. The lower and the upper envelopes are determined by interpola-

tion of si,k(t) between the local maxima and minima, respectively.3. The mean value mi,k(t) of the resulting upper and lower

envelopes is computed and subtracted from si,k(t) so that thenext component of sifting is defined by

si,k+1(t) = si,k(t) − mi,k(t). (1)

4. The component si,k+1(t) is checked against the following IMFconditions and, if it is not been met, sifting continues (steps 1through 3 is repeated with k = k + 1). Condition 1. An IMF, by def-inition, is symmetric in time and has a number of extrema andzero crossings which must be equal or, at most, differ by one.Condition 2. The mean value of the envelopes, defined by thelocal maxima and the local minima, must be zero at all times.

5. The steps above are repeated until the two conditions are met;the resulting IMF is denoted as ci(t). To speed up the procedurecertain stop criteria could be used, e.g. by relaxing second IMFcondition allowing certain deviation from zero or by limitingnumber of sifting steps. It is obvious in both cases sifting resultwill differ to a certain degree from the “true” IMF and should beused with caution.

The next sifting process starts after subtraction of ci(t) from sig-nal si,1(t) so the resulting signal ri(t) is the input to the successivesifting process:

ri(t) = si,1(t) − ci(t), si+1,1(t) = ri(t) (2)

This process is repeated until the residual rN(t) will have less than3 extrema, which means that all IMFs have been extracted. Theobserved signal s(t) can be expressed as a sum of IMFs and theresidual rN(t),

s(t) =N∑

i=1

ci(t) + rN(t). (3)

The empirical mode decomposition process can be understood as astep by step extraction of the locally highest frequency oscillationof the signal, progressively forming the low-pass intrinsic modefunctions. Examples of EMD decomposed ECG and ICG signals arepresented in Figs. 1 and 2 lower figures. The mode mixing effect isclearly seen in e.g. Figs. 1 IMF4, IMF5 and 2 IMF6, IMF7.

2.2. Ensemble empirical mode decomposition

Recently a new ensemble EMD method has been proposed toovercome mode mixing problem [21,18,24,26]. It defines the trueIMF components as the mean of an ensemble of trials, each consist-ing of the signal plus a white noise of finite amplitude. Accordingto this approach, the realization of artificial white noise is added

to analyzed signal and then EMD method is applied on the noisydata. These operations are repeated for a certain number of times,each time adding a new white noise realization to the same signal.Each individual trial produces noisy IMFs, because each input to

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ig. 1. Decomposition of electrocardiogram signal into IMFs. Ensemble EMD resultsn the top graph show IMFs containing majority of QRS complex and T wave comp

he decomposition consists of the analyzed signal and added whiteoise. The output is a set of an ensemble of data decompositionsith added white noise. Since the noise in each trial is different, it

s canceled out by averaging all realizations of each IMF. The finalverage of the corresponding IMFs is treated as EEMD result. Addedoise forces for a uniform scale distribution in each trial and theean of IMFs stay within the natural dyadic filter windows, signif-

cantly reducing chance of mode mixing and preserving the dyadic

roperty [18].

Ensemble EMD method has several a priori parameters: (1)dded noise level and (2) a number of trials. The number ofrials should be a compromise between good noise reduction

own in the upper figure while EMD results are shown in the lower one. Rectangles that could be used for enhancement of these ECG waves.

and computational time. Recently an innovative method hasbeen proposed in order to reduce computational time withoutincreasing the number of trials [24]. According to this algorithm,white noise realizations are added in pairs with positive andnegative signs to the signal. This allows decreasing the number oftrials effectively eliminating residue noise. This improvement wasimplemented in our algorithm, too. Particularly an application of40 trials was used. In order to speed up the procedure, the number

of EMD sifting steps was limited to 40, taking into account largenumber of realizations and the following averaging procedure.The level of white noise may play quite an important role in EEMDapplications. We used white noise with the amplitudes set to

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0 1 2 3Time (s)

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ig. 2. Decomposition of impedance cardiogram signal into the IMFs: EEMD resultshows IMFs containing main energy ICG components that could be used for ICG enh

.3 times of standard deviation of the raw data. This noise levelrovided acceptable results in all analyzed signal cases. The addedhite noise with higher amplitudes could specifically distort the

EMD result when only the larger signal waves appear in the firstMF, whereas the small signal waves are replaced by a noise-scaleaseline and appear in the following IMFs. This EEMD feature issed in adaptive threshold filter applications [25,26].

The examples of the EEMD decompositions of clinically recordedaw ECG and ICG signals without preprocessing and comparison tohe traditional EMD are presented in Figs. 1 and 2. The ensembleMD has provided more predictable IMFs. It is also evident that

hown in the upper figure while EMD results are shown in the lower one. Rectangleent.

EEMD is more suitable for specific signal feature enhancement.Fig. 1 shows that IMF4–IMF6 or IMF4–IMF7 contain the majorityof QRS complex energy while the other ECG signal waves are sig-nificantly suppressed. Intrinsic mode functions 7–9 could be usedfor ECG T wave enhancement. The example in Fig. 2 shows thatIMF7–IMF9 contain the majority of ICG signal energy while almostall the noise and artifacts could be found in the rest IMFs. This is very

important for ICG signal analysis since many of ICG fiducial pointsare calculated from ICG first derivative, which is very sensitive tothe noise. Rectangles show regions that incorporate the majority ofenergy of the analyzed specific signal waves. The observation that

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EMD provides better results for real-life signal processing in com-arison to EMD completely agrees with the results presented inhe other investigations: EMD decomposition spreads the energymong different IMFs in different time instances and it is unpre-ictable which IMFs incorporate energy of the analyzed specificignal waves [18,29,30].

.3. Discrete wavelet transform

Discrete wavelet transform is well established method having solid theoretical background and equipped with the efficientast implementation algorithms [31]. So it has been applied as a

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ig. 3. Wavelet transform scales (levels) are obtained from ECG (upper figure) and ICG (low wave and ICG signal energy and thus they are suitable for feature enhancement.

g & Physics 35 (2013) 1059– 1069 1063

reference method to compare to EEMD. The wavelet transformprovides an efficient time-scale representation for functions whichhave similar shape to the functions in the wavelet basis. Wehave used the orthogonal DWT for signal decomposition on thetime-scale plane, which represents the signal s(n) by:

s(n) =K∑

j=1

∞∑

k=−∞wj(k) (2jn − k) (4)

where the function (n) represents a discrete analysis wavelet andthe coefficients wj(k) represent the signal at level j. In the presentedmethod the Coiflet5 wavelets were used for the decomposition

2 3s)

ECG

d 5

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d 8

d 9d 10d 11d 12

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er figure) signals. Rectangles show scales containing the majority of QRS complex,

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ince they had a shape similar to the analyzed signals [32,15,17].hoice of other wavelet basis could probably provide better results

n the specific cases, however, it would be necessary to find an opti-al wavelet for each signal type, each feature to be enhanced and

n the best case even to each individual signal.Physically, discrete wavelet transform can be understood as a

and-pass octave filter bank which decomposes a signal into scaleslevels) each of which contains signal components with progres-ively lower frequency content (Fig. 3). With this approach, the timeesolution becomes arbitrarily good at high frequencies, while therequency resolution becomes arbitrarily good at low frequency.he main differences between EEMD and DWT decomposition are:

The frequency content of DWT scales is always fixed and dependson sampling frequency and decomposition level (scale), whileIMF may have variable frequency contents depending on localsignal properties.Performance of DWT depends on choice of the wavelet, its simi-larity to analyzed signal, while EEMD has no basic functions andis dependent on the signal itself.

Examples of the decomposed signals using DWT, ECG and ICGre presented in Fig. 3. It is seen that wavelet scales d7–d8 containhe majority of QRS complex energy, while the other ECG signalaves are significantly suppressed. These scales cover frequency

ange approximately from 7.8 Hz to 31 Hz which is typically usedor QRS complex detection in various applications. Scales d8–d10over frequency range approximately from 1.9 Hz to 7.8 Hz and con-ain the majority of T wave energy. In the next noisy ICG signal case,he choice of scales containing ICG components with the highestNR is not so obvious.

.4. Feature enhancement algorithm

In the first instance, the signal was decomposed into intrinsicode functions by EEMD. Then the cross-correlation coefficient�) was calculated between a template signal and each IMF. Thentrinsic mode functions that had higher � than the defined thresh-ld were added together in order to get enhanced output signal. Inur application, the template was a band-pass filtered input signal.utput of the filter must contain the majority of the energy of the

ignal waves to be analyzed. We have used filter bands from 7 Hzo 24 Hz for QRS complex enhancement in ECG signal; from 0.7 Hzo 20 Hz for ICG signal. These frequency bands were found to be

compromise between SNR enhancement and preservation of theignal features [1]. Bi-directional 2nd order-Butterworth IIR filteras used in this application. Formation of the template from the

nalyzed signal itself allowed achieving further adaptation of theethod to the local signal properties.The threshold of the cross-correlation coefficient was adaptively

hosen for each signal type. Only the IMFs that provided higher than the threshold T max(�) were included into the output sig-al. The higher the threshold, the less IMFs would be included intohe output signal resulting in the less fine signal features. Accord-ng to this logic, empirically chosen T was equal to 0.5 for ECGRS complex enhancement. The T value was set to 0.4 for the ICGrocessing case. In this way, the separation thresholds were cal-ulated as T level from maximal � between template signal andMFs. The thresholds were individual for each analyzed record andlso depended on the SNR of the input signal. This means that theumber of IMFs and scales included into the enhanced signal were

ifferent, too. Usually output signals of the proposed algorithm hadider frequency range, higher SNR and fine signal features were

etter preserved comparing to the template signal, in this wayllowing for more precise detection of fiducial points.

Fig. 4. The proposed algorithm for signal feature enhancement.

The reference DWT based method was implemented using thesame algorithm (Fig. 4) with the difference that DWT was used forsignal decomposition instead of EEMD.

2.5. Linear filtering

Linear filtering (LF) is commonly used in various clinical appli-cations. Generally this method is employed for SNR improvementand enhancement of specific signal features in ECG and ICG sig-nals and thus it was used as another reference to DWT and EEMDbased methods. In order to have sharp enough frequency responsecharacteristics and minimize distortion, the 2nd order zero phaseband-pass Butterworth IIR filter was used for the linear filtering. Ineach case, the frequency band was optimized with reference to thefollowing criteria: 0.1–2 Hz to 8–25 Hz in the case of ICG; and from5–9 Hz to 20–30 Hz in the case of the ECG signals for QRS complexdetection.

3. Validation of the algorithm

The two types of cardiological signals: ECG and ICG were usedfor the evaluation of the presented method. In order to analyzeperformance of the method, reference signals, 10 s long clinical ECGand ICG records with relatively high SNR (defined as the logarithmicratio of signal and noise RMS levels) were used. The ECG signalswere of two commonly found QRS form types: monophasic andbiphasic.

In order to estimate sensitivity of the methods to the noise, arti-ficial noise was produced and added to the signals. The followingtypes of noise were included: simulated power line noise, filteredwhite Gaussian noise, which could be an estimation of muscle orelectronic noise, baseline drift noise and the sum of all these noises.The power line noise was simulated as 50 Hz frequency sinusoidwith amplitude equal to 5% of the largest R peak amplitude in therecord. White Gaussian noise was filtered by the band-pass filter atfrequency range of 1–100 Hz and was added to the signal to obtain0 dB SNR. The baseline drift was simulated as the sum of 0.25 Hz and0.3 Hz sinusoids with amplitude equal to QRS amplitude. In ICG sig-nal case artificial noise was defined in a similar manner with thefinal SNR of −12 dB.

Clean ECG and ICG signals and the same signals distorted by alltypes of artificial noise are shown in Fig. 5.

In both ECG signal cases, the fiducial points were annotated bycardiology expert at one representative cardiac cycle.

In order to evaluate the performance of the proposed and refer-enced methods, different criteria were chosen. In this case, the aimof the proposed EEMD based method was to enhance QRS complexin noisy signal. The performance criterion for ECG signal application

A. Janusauskas et al. / Medical Engineering & Physics 35 (2013) 1059– 1069 1065

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noisy signal, with added all types of noise and processed by theinvestigated methods, are shown. QRS length was defined as a timeinterval between the largest extrema on both sides from QRS com-plex with opposite sign to the nearest QRS complex wave. Like in

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ig. 5. Clean biphasic ECG signal is shown at the top of the left side and the same side and ICG with added noise is shown below.

as QRS complex length. This criterion was chosen because afterRS enhancement signal was suitable for detection of QRS com-lex limits but it was distorted and other waves were suppressed.hus estimation of the methods performance based on classicaleasures such as cross-correlation, SNR or signal to error ratio is

ot applicable in this case. Automatically detected QRS complexengths were compared with QRS complex length defined by cardi-logy expert. Error value was quantitative criterion for estimationf EEMD based and reference methods performance.

In ICG case, the criterion was a cross-correlation coefficient cal-ulated between the first derivative of the clean ICG signal and therst derivative of the processed noisy realization of this signal. Onergument for the use of derivative instead of ICG signal is the facthat the majority of fiducial points are clinically calculated fromCG derivative. The second argument is the fact that the derivatives much more sensitive to residual noise and distortion of the signal

orphology. In this case, the cross-correlation criterion is used forCG analysis since signal morphology is more important than abso-ute values. Additional criterion was signal to error ratio expressedn dB:

ER = 10 log

∑x2(t)

∑[x(t) − x(t)]2

(5)

here x(t) is clean template signal and x(t) is noise corrupted signalfter application of the proposed method.

. Results and discussion

The ensemble EMD based method was not sensitive to vari-

us physiological signal artifacts and in most cases it was able torovide better signal quality comparing to traditional filtering tech-ique routinely used in a clinical practice and the reference DWTethod. Some examples of the EEMD based method performance

with added noise is shown below. Clean ICG signal is shown at the top of the right

and comparison with the traditional filtering technique and DWTbased method are presented below.

The example of enhanced monophasic QRS complex for onsetand offset detection is given in Fig. 6. In this case, EEMD and DWTbased methods and traditional filtering techniques were appliedto the ECG signal with monophasic QRSs and all types of noiseadded. QRS length was defined as a time interval between thelargest extrema on both sides from R peak with opposite sign tothe R peak. QRS onset and offset positions detected by the pro-posed EEMD method were denoted by vertical lines. In this specificcase the EEMD enhanced signal resulted in more exact QRS onsetand offset detection comparing to the other methods.

Biphasic QRS case is given in Fig. 7. Clean reference signal and

Time (s)

Fig. 6. QRS complex enhancement for QRS onset and offset detection, monophasicsignal. Bold line denotes original ECG signal, gray – band-pass filtered signal, dashedline – output from DWT and dotted line – output of the EEMD method.

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0 0.25

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Fig. 7. QRS complex enhancement for QRS onset and offset detection, biphasic sig-nl

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ICG features the other signal to error ratio criterion was also used

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al. Bold line denotes original ECG signal, gray – band-pass filtered signal, dashedine – output from DWT and dotted line – output of the EEMD method.

he previous case, the proposed EEMD based method was the mostxact in detection of QRS complex limits. In this example traditionalltering technique provided the similar result.

Results of all investigated signal cases are given in Table 1 andverage results in Table 2. Results were obtained by averagingutomatically detected QRS lengths over the analyzed 10 s ECGecord and comparing to cardiology expert measurement of oneepresentative clean ECG QRS cycle from the same analysis range.he difference between mean QRS complex length and QRS com-lex length measured by expert defined “error”. The worst resultsccording to this criterion for each method and signal morphol-gy are given in bold. Ensemble EMD based method has given theest results in all biphasic QRS signal cases and all monophasicignal cases with added noise. Surprisingly the EEMD method washe most sensitive to the power line noise compared to the otherypes of noise. This was observed in both QRS form cases. Discreteavelet transform has provided the largest error in Gaussian whiteoise and power line noise cases for biphasic QRS and power lineoise case for monophasic QRS. Performance of linear filtering wasostly degraded when all noises were added to the analyzed signal.

his result was predictable.In order to estimate statistical significance between results of

EMD and the reference methods Student’s paired samples t-testas performed in order to compare the methods. The results show

tatistically significant differences between EEMD and referenceethods at the 5% significance level. In Table 1 statistically signif-

cant results are noted by asterisk. Statistically significant results

able 1RS complex length detection results. Mean and error value for each case are presented.

Case Method QRS biphasic, ms

Expert 97

Clean signalEEMD 98

DWT 95

LPF 96

Power line noiseEEMD 101

DWT 92

LPF 93

Gaussian noiseEEMD 95

DWT 102

LPF 93

Baseline wanderEEMD 99

DWT 95*

LPF 96*

All noise typesEEMD 97

DWT 101*

LPF 102*

* Statistically significant difference (p < 0.05) between reference and EEMD methods.

g & Physics 35 (2013) 1059– 1069

were obtained for both ECG signal morphology types for baselinewander noise and for three of four cases when all noises were addedto the reference signal.

The simulation results are summarized in Table 2. In thistable, the mean represents systematic error of the methodswhile standard deviation estimates stability of the methods whenprocessing signals corrupted by various artifacts.

Ensemble EMD method outperformed other investigated meth-ods in the sense of average error for both monophasic and biphasicECG signal cases and mean value for monophasic signal case.Standard deviation parameter is also very important since it pro-vides estimation of the method stability when analyzing varioussignals with different SNR and noise types. The smallest standarddeviation for biphasic QRS signal and monophasic QRS signal caseswas also obtained by using EEMD based method. In both cases, DWTprovided the most dispersal results. The analysis of obtained resultssupports the conclusion that the performance of EEMD methodin general is better comparing to the other investigated methodswhen analyzing noisy ECG signals.

The example of clean and processed ICG signal and its derivativeis given in Fig. 8. The case when all noises added (SNR = −12 dB) toICG signal is given, since in all other cases the results were very sim-ilar. Ensemble EMD based method provided the most exact matchbetween clean ICG derivative and processed noisy signal.

Full ICG derivative signal as obtained from all the methods anderror signals are given in Fig. 9. Root mean square errors were3.88 for EEMD method, 9.32 for DWT and 4.14 for linear filter-ing method. Since dZ/dt signal morphology is very important forclinical investigation the cross-correlation criterion was also usedin this case. The cross-correlation coefficient was equal to 0.98for ensemble EMD method. Linear filtering produced additionalripples in ICG derivative which could introduce incorrect detec-tion of fiducial point positions. The cross-correlation coefficientbetween derivative of clean ICG and processed noisy ICG deriva-tives, however, was high and equal to 0.97. The worst result wasprovided by DWT since it is possible to observe that some of sig-nal peaks disappeared while the others were advanced or delayed,see Fig. 9. Not adequate mother wavelet morphology could be oneof the possible reasons for observed effects. The cross-correlationcoefficient in this case was equal to 0.91. Since the aim of themethod was rather to de-noise signal than to enhance specific

for the estimation of the methods performance. Signal to errorratio after processing signal with the initial SNR of −12 dB was33 dB.

Error, % QRS monophasic, ms Error, %76

1.03 71 6.582.06 74 2.631.03 82 7.89

4.12 81 6.585.15 67* 11.844.12 71* 6.58

2.06 72 5.265.15 82 7.894.12 84 10.53

2.06 78 2.632.06 80* 5.261.03 81* 6.58

0 77 1.324.12 84 10.535.15 86* 13.16

A. Janusauskas et al. / Medical Engineering & Physics 35 (2013) 1059– 1069 1067

0

ICG

(Ω

)

0 1 2 3

0

Time (s)

dZ/d

t (Ω

/ s)

Fig. 8. Original and processed ICG and ICG derivative (dZ/dt) signals. Bold line denotes original ICG signal, gray line – band-pass filtered signal, dashed line – the output fromthe DWT based and dotted line – the output from the EEMD methods.

−5

0

5

10

Err

or o

f EE

MD

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5

10

Err

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T

2 4 6 8 10−5

0

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Time (s)

Err

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f BP

filte

r

−5

0

5

10

dZ/d

t

Fig. 9. Top figure ICG derivative (dZ/dt) of the clean signal and derivatives of the noisy signal after signal enhancement. Bold line denotes original ICG signal, gray line –band-pass filtered signal, dashed line – output from the DWT based and dotted line – output from the EEMD methods. Error signal for all the methods are given in figuresbelow.

1068 A. Janusauskas et al. / Medical Engineering & Physics 35 (2013) 1059– 1069

Table 2Result’s average for detection of QRS complex length.

Method QRS biphasic QRS monophasic

Mean, ms STD Aver. error % Mean, ms STD Aver. error %Expert 97 76

EEMD 98 2.24 1.85 75.8 2.39 4.47DWT 97 4.3 3.71 77.4 3.77 7.63LFT 96 3.67 3.09 80.8 2.85 8.96

0 1Time (s)

MAX

X Y

O

Fig. 10. Application to raw clinical ICG. ICG signal, band-pass filtered signal and outputs of the EEMD and DWT based methods are shown in the upper figure. Middle figureshows the same signals only with narrower band-pass filter frequency range in order to achieve smooth signal. dZ/dt signal from band-pass filtered ICG as shown in a middlefi st figug dotte

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gure and the output from EEMD and DWT based methods are shown in the loweray line – band-pass filtered signal, dashed line – output from the DWT based and

The real life illustrations of ICG processing results are shownn Fig. 10. It is clear that the band-pass filtered signal contains too

uch noise ripples for ICG derivative (dZ/dt) calculation. In ordero get smoother signal, high cut frequency of the filter was loweredntil the signal became smooth enough for dZ/dt calculation (a setf the signals in the middle of Fig. 10). However, the fine featuresf the ICG signal disappeared in this case which resulted in miss-ng X and Y fiducial points in the derivative signal, as it is seen inhe lowest set of the signals in Fig. 10. The DWT method distorteduite significantly ICG signal which resulted in additional ripplest certain time intervals in ICG derivative and at the same timever-smoothed other dZ/dt signal parts. The EEMD method, on theontrary, enhanced ICG signal in a way when all the fiducial pointsan be easily detected in ICG derivative signal.

Generally all three investigated methods showed very similarerformance in cases when ECG signals had enough high SNR (clin-

cal ECG signals used for modeling). In low SNR cases, the EEMDethod provided more exact results with less QRS onset and offset

ndershoots and overshoots according to cardiology expert anno-ation to compare to the other implemented methods. There werelso a few cases when it was very hard to distinguish QRS fromhe other signal waves and noise artifacts when using DWT based

ethod. This observation is even more applicable to ICG signals.

. Conclusions

The algorithm presented here introduces the way to control

EMD method, allowing to enhance chosen waves in the analyzedignal. Many of the EMD and EEMD applications are adapted to thepecific recording equipment or certain signal morphologies sincere using fixed scales [27,28,19] which limit real life applications

re together with a few dZ/dt fiducial points. Bold line denotes original ECG signal,d line – output from the EEMD methods.

of these methods. Some other EMD and EEMD applications use ref-erence sophisticated methods or synthetic templates [33,29]. Ourmethod is fully adaptive to the signal since it uses the signal itselfas variable template and offers simple way how to control the fea-tures that need to be enhanced. Our investigation shows that theproposed approach is suitable for SNR enhancement, too.

The new method was applied to ECG and ICG signals; how-ever, the range of its possible applications is much wider. In thepresented application, the method uses filtered input signal as atemplate in order to achieve adaptation of the method to the localsignal properties, however, it is possible to use templates gener-ated in other ways. It is possible to control which IMFs shouldbe included into the output for the following analysis by choosingtemplate. The method also offers some more degrees of freedom,allowing a user to choose desired characteristics of the outputsignal. Template, constructed by using an input signal, has anadvantage of following local changes in signal; in this way reduc-ing probability of missing important events when signal changes.Using various noise levels of the added white noise or � thresholdlevel, it is possible to control sensitivity of the method to the finesignal features (see Fig. 4, Section 2.4). In this way the method couldeither extract or enhance fine features of the signal or to leave onlythe waves with the highest amplitude. The innovation of the pro-posed algorithm is adaptation of EEMD method for cardiologicalsignal processing i.e. the usage of template adaptive to analyzedsignal together with cross-correlation technique for enhancementof the preferred signal features.

The presented application of EEMD based algorithm showedgood results – both in ECG and ICG signal feature enhancementand in general outperformed traditional filtering technique andDWT based algorithm. The differences between the new and the

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A. Janusauskas et al. / Medical Eng

lassical algorithms were more pronounced in the signals withower SNR. Presented algorithm is used as a part of softwareor automated analysis of synchronously recorded ECG, ICG andeismo-cardiogram signals.

cknowledgements

Competing interests: None declared.Funding: None.Ethical approval: Not required.

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