Proceedings of the 9th International Conference on Info~ation Technology andApplications in Biomedicine, ITAB 2009, Larnaca, cyprus, 5-7 November 2009

Methods for Evaluation of Central Hemodynamics and Detection ofIndicators of Risk of Sudden Cardiac Death for Network Based

Clinical Decision Support System

Algimantas Krisciukaitis, Renata Simoliuniene, Andrius Macas, Giedre Baksyte, Remigijus Zaliunas

Abstract- Two methods for evaluation of crucial factorsdescribing status of cardiologic patients based on advancedsignal processing methods were incorporated into prototypenetwork based clinical decision support system: a) novel methodfor chest impedance signal analysis enabling reliable evaluationof central hemodynamics in non-invasive way; b) method forautomatic detection and evaluation of ECG T-wave alternans ­predictor of sudden cardiac death. Both methods supplementeach other and improve the quality of monitoring of patientstatus in intensive care unit.

Index Terms- lCG, T-wave alternans, PrincipalComponent Analysis

I. INTRODUCTION

M ONITORING and quantitative evaluation of centralhemodynamics together with automatic detection of

risk factors of sudden cardiac death are of crucial importanceduring acute phase of myocardial infarction. Two methods ofadvanced electrocardiosignal (ECG and chest impedance ­ICG) processing were incorporated into network basedclinical decision support system in aim to extend possibilitiesof standard monitoring equipment used in the intensive careunits.

Cardiac output is one of the core parameters in assessingthe status of the patient in acute phase of myocardialinfarction. Impedance cardiography has been introduced bySramek in the 1960's as a simple and non-invasivemeasurement of cardiac output which is used till nowadays.

Manuscript received June 29, 2009. Lithuanian State Science and StudiesFoundation has supported this work.

A. Krisciukaitis is with Kaunas University of Medicine, Lithuania(corresponding author, phone: +370-37-302952; fax: +370-37-302959; e­mail: algimantas.krisciukaitis@kmu.lt).

R. Simoliuniene, is with Kaunas University of Medicine, Lithuania (e­mail: renatasim@gmail.com).

A. Macas is with Clinics of Kaunas University of Medicine, Lithuania (e­mail: andrius.macas@kmuk.lt)

G. Baksyte is with Clinics of Kaunas University of medicine, Lithuania(e-mail: giedre@caramail.com).

R. Zaliunas is with Kaunas University of medicine, Lithuania (e-mail:remigijus.zaliunas@kmu.lt).

978-1-4244-5379-5/09/$26.00 ©2009 IEEE

However, measured data in some cases remain controversial.This is highly expressed in states causing low cardiac outputsyndrome cardiogenic shock, severe arrhythmias as well as inhealthy obese patients. It led to elaboration and introductionof invasive methods for evaluation of hemodynamics intoclinical practice in 1970. The method of thermo dilution,using Swan-Ganz catheters became a "golden standard" forthe evaluation of hemodynamic changes. However, risk ofcomplications during application of invasive methods andtheir influence on the outcome of a patient's health causednew wave of investigations the aim of which wasimprovement of non-invasive methods of evaluation ofcardiac output based on ICG analysis. Our earlier approachapplying advanced signal processing methods for analysis ofICG signal gave promising results significantly increasingreliability of estimates of central hemodynamics obtained innon invasive way even in complicated clinical cases [1].

T-Wave Altemans (TWA) is reported to be a reliablepredictor of ventricular sudden cardiac death [2], so timelydetection and evaluation of it could help clinicians to takeproper actions and save life for the patient. Our approachapplying special structural analysis of ECG in combinationwith Principal Component Analysis (PCA) showed apossibility of reliable detection and evaluation of TWA [3].

The two above mentioned methods are based on advancedsignal processing methods requiring high powercomputational resources which could be accessed viaspecialized network giving better performance and quickerevaluation results. On the other hand, the standard signalregistration equipment usually available in the intensive careunits of cardiological clinics connected to the networkbecomes sufficient to implement the advanced methods intopraxis.

II. METHODS, DESCRIPTION OF THE SYSTEM

Signals for investigation were registered during 24h followup of the patients in acute phase of myocardial infarction inCardiology Clinics of Kaunas University of medicine(Permission of Kaunas Region Ethics Committee forBiomedical Research Nr. 169/2004). Reference estimates of

B. ECG preprocessing: T-wave extraction

Standard leads of ECG were recorded using DatexEngstrom CS/3, S-ARR97 cardio-monitoring system using 12bit resolution AID conversion at 500 Hz sampling rate.Approximately two minutes length recordings in size up to1.5MB were uploaded to the central server for the processing.Signal preprocessing we started with detection of the fiducialpoint of each cardiocycle - R-wave. A value of short intervalbetween the end of T-wave of preceding cardiocycle andbeginning of P-wave of current cardiocycle, evaluated as amean of 10 consequent samples starting from 170th msbefore the fiducial point was considered to be a baselinereference point of each cardiocycle. Bicubic splineinterpolation using these reference points was used tocalculate baseline wander component, which was subtractedfrom the original signal in each lead. The 160 samplesstarting from 22nd sample after fiducial point wereconsidered as excerpt reflecting repolarization process in eachcardiocycle (ST-T complex). The length of QT intervaldepends on heart rate. We applied time stretching of theordinary ST-T interval using bicubic spline interpolation,maximizing cross-correlation with the template constructedfrom the first 10 cardiocycles in each recording. Estimatedcoefficients for QT interval time stretching were close to thevalues reported by Sagie et al. [5], proposed as substitution ofclassical Bazett formula. Corrected (stretched) arrays ofsamples from each registered lead of the same cardiocyclewere concatenated together forming one array. Theconcatenated arrays of all cardiocycles formed 2-dimensionalarray of samples representing variety of the shape of ST-Tcomplexes from the particular recording considered foranalysis.

myocardial infarction with thermo dilution ones revealedsubstantial errors. The novel method for ICG signaldecomposition is based on combined structural analysis ofsynchronically registered ECG and ICG signals. Respiratorymovements caused component of the signal is restored bymeans of cubic spline interpolation between the samples ofICG signal at the time points, corresponding to the maximumof ECG R-wave - the fiducial point of each cardiocycle.Subtraction of this component from ICG signal gives thecomponent reflecting only blood volume changes in the chestvessels. The principle of the method is illustrated on fig.I(left). Changes of resistance of the lungs' alveoli to the bloodflow during respiratory movements also result variation inshape of the component reflecting blood volume changes (seefig.1 right). It should be considered as additionalsubcomponent in the signal reflecting blood volume changesin the chest vessels.

B

cardiac output we obtained using Swan-Ganz flow-directedtriple-lumen catheter ("Baxter"TM) inserted through eithersubclavian or internal jugular vein with continuous pressureand electrocardiographic monitoring. Measurements wereperformed by the standard procedure flushing 10 ml ofsodium saline injectate (injection speed 2.5 ml/s) viaproximal lumen of pulmonary artery catheter. Cardiac outputcalculated by means of "Datex-Engstrom CS/3" systemhemodynamic module.

A. Chest impedance signal preprocessing.

Chest impedance signal together with ECG signal wasregistered using "HeartLab" system [4]. We used synchronicrecording ofICG and ECG (one lead) using 12 bit resolutionAID conversion at 1000 Hz sampling rate. We analyzed 24hrecords during our previous investigations which wereapproximately 600MB in size. Due to the limited possibilitiesof network data transfer in this study we used 10 minfragments of the recordings (about 4MB in size) registeredevery 3 hours uploading them to the central server and madefurther analysis on concatenated array of such fragments.

Method for decomposition of ICG signal, based oncombined analysis of ICG and ECG signals was used tocalculate two components of ICG signal: one reflectingrespiratory movements, another - blood flow in lungs andmajor vessels of the chest. Genesis ofICG signal is explainedas impedance variations determined by varying blood volumein the main chest vessels during various phases of cardio

cFig. I. On the left - ICG signal decomposition: (A) bicubic splineinterpolation between samples of ICG signal corresponding to ECG R­wave, the fiducial point of cardiocycle; (B)- respiratory movementsreflecting component; (C) - blood volume changes reflecting componentof ICG signal. On the right - variety of ICG cardiocycles in variousphases of respiration.

cycle. Another important factor is respiratory movementswhen physical parameters of chest (amount of air in lungs,resistance of the alveoli to the blood flow etc.) are changing.These two processes result ICG signal consisting of twocomponents with different frequency parameters. Firstderivative of the ICG signal obtained in most cases by meansof special registering hardware mainly reflects blood volumevariation in major chest vessels and is usually used for centralhemodynamics evaluation. However comparison of cardiacoutput estimates obtained using this method in acute phase of

1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 21 2 3 25 27 29 31 3 3 35 3 7 3 941 4 34 547 49

Measu res , n r.

cardiography method in most non-complicated cases.However our main interest was in cases where evaluations ofcardiac output by means of thermo dilution and impedancecardiography disagree. Figure 2A represents serious casewhere evaluation was very important, but unfortunately themethods significantly disagree. Correlation between estimatesis close to zero. First coefficient of principal componentcalculated from this recording was reflecting mostlyrespiratory movements and there was no trend in valuescorrelating with estimates obtained by means of thermodilution. However second coefficient of principal componentshowed correlation of 0.75 with estimates of thermo dilution.The shapes of 1SI and 2nd principal components depicted onthe fig.2B suggest that fine changes in shape of ICG

w

-0- w2 (0,7 498)- TD --<>- dZ / d t( -O ,1906)

Aco, 11m in

Fig. 2. A: Comparison of cardiac output (CO) estimates obtained bymeans of standard ICG method (dZ/dt) and thermo dilution (TO) togetherwith the coefficient of second principal component of ICG cardiocycle(w2). Correlation with thermo dilution is shown in brackets. B: Theshapes of l" and 2nd principal components. C: Contribution of theparticular principal components to the shape ofICG cardiocycle,

cardiocycles could reflect important changes in cardiacoutput. Contribution of 2nd principal component to the shapeof ICG cardiocycles is shown on fig.2C. Standard ICGevaluation method, determining only amplitude of firstderivative is not enough sensitive for such morphologychanges and gives bad result. Fine changes in ICGcardiocycle shape are highly expected in cases of myocardialinfarction because of local ischemic injury of workingmyocardium. These changes could reveal very importantinformation about status of the heart muscle . Therefore PCAapplied for chest impedance cardiocycles could givequantitative estimates of these changes.

B. Detection and evaluation ofT-wave alternans.

Testing of the method for detection and analysis of TWAincorporated in the network based system we started usingspecial set of ECG recordings containing TWA episodesavailable in "PhysioNet" website [6]. As expected, TWA was

Xl,. Xl,2 XI,n (I)x= Xl ,l X2,l X2,n

Xi,j

Xp,l Xp,2 Xp,n

A. Chest impedance signal preprocessing.

We expected that coefficients of calculated principalcomponents of ICG should separately reflect particularchanges of shape of cardiocycles. In most cases variation ofISI coefficient was visually correlating with respirationmovements reflecting signal. The trend of changes of 1SI

coefficient also was correlating with changes of cardiacoutput evaluated by means of standard impedance

Where Xi.} is the ith sample of the jth cardiocycle. The PCAtransforms the original data set into a new set of vectors (theprincipal components) which are uncorrelated and each ofthem involves information represented by several interrelatedvariables in the original set. The calculated principalcomponents are ordered so that the very first of them retainmost of the variation present in all the original variables.Thus it is possible to perform a truncated expansion of ICGcardiocycles or ECG T-waves representing vectors by usingonly the first several principal components . Every vector Xi

representing ordinary ICG cardiocycle or ECG T-wave isthen represented by linear combination of the principalcomponents f/Jk multiplied by coefficients Wi.k:

pxi = l: wik<j)k (2)

k=l '

Variation or trend of coefficients W i.k represents changes ofshape of evaluated ICG cardiocycles or T-wave shapes. It isexpected that dynamics of cardiac output will be reflected bythe shape changes of ICG cardiocycles and represented bychanges of one or several coefficients. Also TWA is expectedto be represented by beat-to-beat altemance of one or mostlyfew coefficients .

C. Evaluation ofthe shape variation ofthe extracted lCGand ECG T-wave cardiocycles.

In both cases samples of extracted cardiocycles giveredundant but comprehensive representation of the signalshape. Changes in shape reflect either changing cardiacoutput in case of ICG component, either altemans ofrepolarization process in case of ECG T-wave. Quantitativeevaluation of these changes gives valuable diagnosticparameters . For both types of signals we applied principlecomponent analysis (PCA) to reduce dimensionality of therepresentations expecting to get one or mostly severalprincipal components reflecting desirable changes. SamplesofICG cardiocycles or ECG T-waves formed array:

III. RESULTS

reflected by altemans of one, or in some cases fewcoefficients of principal components. This specific alterationwas absent in the recordings with no TWA. Values of first 10coefficients of principal components are presented in fig.3A,B. Graph A shows coefficients calculated from synthetictest recording, with model TWA, while real recording showsnot so clear alteration on B. The variety in T-wave shapeshown on graph C (concatenated array of extracted T-wavesfrom two leads).

During our previous investigations [3] we used detection ofseparate clusters of values coefficients representing somedetermined shapes of T-wave between which the altemans istaking part, however testing of the method with more clinicaldata showed that only normalized estimate of power spectral

1 0 _

~o 46 60 86 100 120

Fig. 3. Coefficients of principal components reflecting TWA fromsynthetic ECG recording (A) and clinical one (B), together with varietyof shapes of registered T-waves from clinical recording (twoconcatenated T-waves in different leads from the same cardiocycles).

density of the coefficients at the highest frequency isreasonable quantitative estimate of TWA. The value wasconsidered only in cases when it was at least two times biggerthan mean of 10 neighboring lower frequency estimates.

IV. DISCUSSION

Both described methods were elaborated and tested duringour previous investigations. Network based realization of themethods faced limitations of network data transfer. Usage ofconcatenated fragments of ICG and ECG recordings insteadof 24h recordings was acceptable for the analysis, howeverdetermination of optimal fragment length and how often theymust be recorded needs additional investigations, of coursetaking into account that network data transfer capacities arerapidly developing.

Overall performance of implemented methods in terms ofdata transmission and processing speed could be valued asacceptable for clinical usage. However design of user friendlyinterface needs additional investigation involving clinicaldoctors and remains in future plans. One of the key points inthis work is realization of possibility of interactive control ofall steps of signal processing. The final result is very muchdependant on the quality of signal preprocessing (formationof array of ICG cardiocycles or ECG T-wave fragments forPCA), which takes about 85% of work estimated by total

processor usage. So, there always exists a risk ofpreprocessing errors influencing final result.

Two above described methods were incorporated intonetwork based clinical decision support system previouslyreported in [7,8]. These methods for evaluation of crucialfactors describing status of cardiologic patients were added assupplement to the existing system forming the module formonitoring of the patient status during acute phase ofmyocardial infarction. The methods to be incorporated intoour network based system were selected according to our andother authors' experience using stand alone methods invarious computerized systems. Availability to use all thesemethods having a standard monitoring equipment connectedto the network significantly increases possibilities of thedoctor in particular place. On the other hand, network accessto the wide range of methods helps to reveal most useful onesand improve them using experience of many users.

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