Analysis of the QRS Complex for Apnea-Bradycardia Characterization in Preterm Infants

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1 Author manuscrpt, publshed n "Conference proceedngs : Annual Internatonal Conference of the IEEE Engneerng n Medcne and Bology Socety. 2009;:946-9" DOI : 0.09/IEMBS Analyss of the QRS Complex for Apnea-Bradycarda Characterzaton n Preterm Infants M. Altuve, Student Member, IEEE, G. Carrault, J. Cruz, A. Beuchée, P. Pladys, A. Hernández Abstract Ths work presents an analyss of the nformaton content of new features derved from the electrocardogram (ECG) for the characterzaton of apnea-bradycarda events n preterm nfants. Automatc beat detecton and segmentaton methods have been adapted to the ECG sgnals from preterm nfants, through the applcaton of two evolutonary algorthms. ECG data acqured from 32 preterm nfants wth persstent apnea-bradycarda have been used for quanttatve evaluaton. The adaptaton procedure led to an mproved senstvty and postve predctve value, and a reduced jtter for the detecton of the R-wave, QRS onset, QRS offset, and so-electrc level. Addtonally, tme seres representng the RR nterval, R-wave ampltude and QRS duraton, were automatcally extracted for perods at rest, before, durng and after apnea-bradycarda epsodes. Sgnfcant varatons (p<0.05) were observed for all tme-seres when comparng the dfference between values at rest versus values just before the bradycarda event, wth the dfference between values at rest versus values durng the bradycarda event. These results reveal changes n the R- wave ampltude and QRS duraton, appearng at the onset and termnaton of apnea-bradycarda epsodes, whch could be potentally useful for the early detecton and characterzaton of these epsodes. nserm , verson - 0 Dec 2009 A I. INTRODUCTION PNEA-BRADYCARDIA epsodes are often observed n preterm nfants. The repetton of these epsodes has been assocated wth a poor neuromotor prognoss at 3 years [] and has been dentfed as a predsposng factor to sudden-death syndrome n newborns [2]. Furthermore, these epsodes extend the hosptalzaton perods and occasonally requre telemontorng at home. Therefore, n neonatal ntensve care unts, preterm nfants undergo contnuous cardorespratory montorng to detect apnea-bradycarda epsodes and to ntate quck nursng actons. Manual stmulaton s the most common way to stop apnea-bradycarda epsodes n preterm newborns, however, the nterventon delay measured from the actvaton of the montorng alarm to the applcaton of the therapy remans long [3]. The cardac cycle length (RR nterval) extracted from the electrocardogram (ECG) s generally used to detect apneabradycarda epsodes. However, other parameters extracted from the ECG, lke R-wave ampltude and QRS complex duraton, could be also ntegrated n a new detecton approach. Therefore, n ths paper, three tme seres (RR, R-wave ampltude and QRS complex duraton) were studed for perods at rest, before, durng and after apnea-bradycarda epsodes. To extract these seres from the ECG, a QRS detector algorthm [4] followed by an ECG segmentaton method [5] were appled. However, these methods were conceved for the analyss of adult ECG and should be adapted to the specfc characterstcs of the newborn's ECG. Evolutonary algorthms (EA) were chosen to realze these mportant steps. II. METHODS A. Apnea-bradycarda ECG database Data were obtaned from 32 premature nfants, who presented more than one bradycarda per hour and/or the need for bagand-mask resusctaton. At the moment of the recordng, the medan brth weght was 235 g, the medan age was 3.2 weeks and the postnatal age was 2. days. Recordngs were acqured usng the PowerLab /Chart v4.2 system and conssted of a -hour recordng at a 400-Hz samplng rate of one lead ECG [6]. Bradycarda events were detected and annotated by analyzng the RR nterval. A bradycarda epsode was defned as RR 600 ms durng 4 s or more [7]. Two database subsets were constructed: DB: 50 ECG segments defned from 5 mnutes before the begnnng of a bradycarda untl 2 mnutes after the end and contanng only one bradycarda event durng ths whole perod. Only 27 patents presented at least one epsode as descrbed above. In DB 5655 R-waves postons have been annotated. Manuscrpt receved Aprl 23, Ths work was supported n part by the ECOS project. M. Altuve s wth the Department of Industral Technology, Smon Bolvar Unversty, Caracas, Venezuela, wth the INSERM, U642, Rennes, France, and also wth the LTSI, Unversty of Rennes, France. (phone: ; e-mal:mguel.altuve@unv-rennes.fr, maltuve@usb.ve) G. Carrault and A. Hernandez are wth the INSERM, U642, Rennes, France, and also wth the LTSI, Unversty of Rennes, Rennes, France (e-mal: guy.carrault@unv-rennes.fr, alfredo.hernandez@unv-rennes.fr). J. Cruz s wth the Department of Electronc and Crcut, Smon Bolvar Unversty, Caracas, Venezuela (e-mal: julocruz@usb.ve). A. Beuchée s wth the CHU Rennes, Department of Pedatrcs, Rennes, France, and also wth the INSERM, U642, Rennes, France, (e-mal: alan.beuchee@chu-rennes.fr). P. Pladys s wth the CHU Rennes, Department of Pedatrcs, Rennes, France, wth the INSERM, U642, Rennes, France, and also wth the LTSI, Unversty of Rennes, France (e-mal: patrck.pladys@chu-rennes.fr).

2 nserm , verson - 0 Dec 2009 DB2: 93 ECG segments randomly chosen from the entre database, but dfferent from DB, wth at least one ECG segment per patent. DB2 s characterzed by normal heart rate (HR) and by one or more bradycarda epsodes per segment. In DB2 the poston of the R-wave, QRSon, QRSoff and the so-electrc level have been annotated n 4464 beats. B. Beat detecton process In the QRS detecton algorthm [4], the ECG sgnal s processed by a cascade of low-pass and hgh-pass flters (cutoff frequences fc Low and fc Hgh ), followed by a double dfferentator flter, an ampltude squarng process and a movng-wndow ntegrator of wdth T MWI. The fnal step s based on adaptve thresholds, whch are contnually adjusted by a set of heurstc rules, to track the changes on the ECG sgnal. Opposed to [4], a buffer (T Buff ) to collect the tme hstory of the sgnal and the peak values of the transformed sgnal obtaned after the movng-wndow ntegrator, and one set of thresholds referred to these peak values were used. The adaptve threshold (THR) s found by usng THR = βpeak + δ αpeak βpeak ) () ( where Peak s the average of the NP most relevant peaks determned by usng NP Peak = λ Peak (2) NP = and δ, α, β, and λ are constants. Peaks greater than THR are consdered as a QRS complex. The algorthm apples a refractory perod (T Refr ) and a search wndow (T Peak ) n the band-pass fltered sgnal for fducal pont (FP) detecton. If a QRS s not found durng T RRlm, the parameters are reset and a new QRS detecton process begns from the last QRS correctly detected. Table I summarzes the parameters of the beat detector to be optmzed. C. Automatc QRS segmentaton process In the wavelet transform (WT) segmentaton method [5], each detected beat s extracted from the ECG and delmted nto a small temporal support around the QRS complex. Beat templates are created by the average of the most recent beats, detected n a tme hstory of 0 seconds. Only beats presentng a normalzed cross-correlaton hgher than 0.96 are used to update the beat template. The updated template s decomposed n fve scales wth an octave flter bank wthout decmaton. Several search wndows are used to fnd waves boundares, e.g. T R and T R2 to dentfy the R-wave, T Qlm to fnd the Q-wave, and T Slm for S-wave. By usng (3), temporal parameters can be found as a scaled verson (m) of the RR nterval, for R, R2, Qlm, Slm. T = m RR (3) Two thresholds (γ QRSpre and γ QRSpost ) are used to fnd sgnfcant slopes of the Q and S waves. ξ QRSon+ (or ξ QRSon- ) and ξ QRSoff+ (or ξ QRSoff- ) are thresholds used to fnd QRS onset (QRSon) and offset (QRSoff). As proposed n [8], the so-electrc level poston (ISOp) s determned as the flattest waveform of sze T Iso found on the tme-wndow of T PQ seconds precedng the R- wave. T PQ and T Iso can also be represented as a functon of the RR nterval by usng (3). D. Parameter optmzaton based on EA The parameters of the beat detecton and WT segmentaton methods have been adapted to the processng of ECG sgnals acqured from preterm nfants. Ths problem can be vewed as the mnmzaton of a cost functon defned between the observaton of the events (wave annotatons) and the algorthm output (wave detecton). Evolutonary algorthms (EA), optmzaton methods nspred from natural selecton, have shown to be well adapted to solve ths knd of multdmensonal problems [9]. A smlar optmzaton problem of a sgnal processng chan, presentng n detal the optmzaton methodology, has recently been publshed by our laboratory [5]. Two ndependent EA were sequentally appled: EA optmzes the parameters of the beat detector and EA2 optmzes the parameters of the QRS segmentaton method. Such a parttonng s possble because the QRS segmentaton wll be optmal only f the beat detector s prevously optmzed. The cost functon (C) to be mnmzed by each EA s gven by: I ( DJ + σdj Perr ) C = µ + (4) = for FP, R-wave, QRSon, QRSoff, ISOp. It combnes three crtera: ) mean detecton jtter (µdj) computed as the average of the jtter between the annotaton and the detecton over all ECG segments; ) standard devaton of the detecton jtter (σdj) determned as the average of the standard devaton of the detecton jtter of each segment over all ECG segments; and ) error detecton probablty (Perr) calculated by usng: Perr ( S ) 2 + ( PPV ) 2 = (5) where S = senstvty, PPV = postve predctve value, FP, R-wave, QRSon, QRSoff, ISOp. To create the ntal populaton for EA, parameters to be optmzed were ncreased and decreased from [4], whereas for EA2, scaled parameters defnng the dfferent temporal supports were defned from possble extreme postons and duratons of each wave and scaled parameters related to thresholds were ncreased and decreased from [5]. Rankng selecton method,

3 nserm , verson - 0 Dec 2009 smple, arthmetc and heurstc crossover, and mult-non-unform and non-unform mutaton were used [9]. Both EA were appled for 80 generatons wth 200 ndvduals, wth a probablty of crossover of 0.7 and a probablty of mutaton beng hgh durng the frst generatons and low at the end [0]. A performance comparson before and after parameter optmzaton was made, by evaluatng the senstvty, the PPV, the µdj and the σdj on the test sets. E. QRS complex analyss Seres of RR, R-wave ampltude (R Amp ) and QRS duraton (QRS Dur ) were determned and analyzed for each ECG segment from DB. Four ntervals were used for analyzng each seres: ) T: from 5 mnutes before the bradycarda untl the second mnute, contanng the HR n rest (wthout any perturbaton related to an apnea-bradycarda event); ) T2: from mnute 3 to 5, wthout bradycarda but the apnea epsode has already begun; we would lke to fnd some relevant nformaton that arrves just before the bradycarda event; ) T3: durng the bradycarda event (apnea and bradycarda epsodes are present); and v) T4: from the end of the bradycarda and wth a duraton of 2 mnutes, where, generally, the HR returns to ts rest value. R Amp tme-seres were normalzed by dvdng by the hghest value found n nterval T. The weghted mean (wµ) and the weghted standard devaton (wσ) were computed for each nterval for all tme seres. The average of the absolute dfference of the mean (µad) between values on nterval T (consdered as reference) and the other ntervals was calculated for all segments as follows: X ( x) µ TS ( x) µ TS T T x= µ AD T T = (6) X where TS RR, R Amp, QRS Dur ; 2, 3, 4; X are the avalable ECG segments; µts(x) T s the mean of TS computed for each segment x of each nterval T. The average of the absolute dfference of the standard devaton (σad) was estmated n a smlar fashon. Mann Whtney U statstcal hypothess tests was used to analyze the varatons between ntervals, where a p-value of p<0.05 s consdered sgnfcant. III. RESULTS Results are presented n tree parts: ) condtons for the applcaton of the EA, ) the performance of the beat detecton and WT segmentaton methods, and ) the QRS complex analyss for apnea-bradycarda characterzaton. A. Evolutonary Algorthm Two learnng sets (LS and LS2) and two test sets (TS and TS2) were constructed to carry out the optmzaton: LS: used for EA and composed of 2500 beats (50 beats per segment) obtaned from DB, where the frst 25 ECG segments, extracted from the frst part of each ECG segment, do not present any bradycarda epsodes (mean RR nterval of ± 3.02 ms), whereas the other 25 ECG segments present bradycarda epsodes (mean RR nterval of ± 6.79 ms). LS2: used for EA2 and composed of 2256 beats from 47 ECG segments obtaned from DB2. In ths set, 34 ECG segments (632 beats) present a normal HR (RR nterval of ± 6.06 ms), the other 3 ECG segments (624 beats) contan bradycarda epsodes (RR nterval of ± 5.4 ms). TS: used to test the optmal parameters found for EA and composed of the entre DB. TS2: used to test the optmal parameters found for EA2 and composed of 2207 beats from the rest of the 46 ECG segments obtaned from DB2. Ths set presents a normal HR (RR nterval ± 8.34 ms). Several QRS morphologes are present n these datasets. B. Beat detecton and WT segmentaton performance Parameters of the beat detector, before and after the optmzaton methodology, by usng EA on LS, are presented n Table I. Compared to those used n adults by [4], optmal parameters show an ncrease n the cutoff frequences of the lowpass and hgh-pass flters that obvously are related to the fact that the QRS of preterm nfants are generally thnner and have hgher frequency content than the QRS of adults. Also, t s observed a decrease n the sze of the wndow for movng-average ntegraton that can also be explaned by the hgher frequency content of the newborn's QRS. These parameters have been used to evaluate the performance of the QRS detecton method on TS. Optmal parameters related to temporal search wndows of the WT segmentaton process, by usng EA2 on LS2, are: m R =0.2, m R2 =0.099, m Qlm =0.003, m Slm =0.70, m PQ =0.92, m Iso = An example of the optmal parameters, usng a typcal RR nterval of 400 ms s llustrated n Table II. A comparson between our approach and [5], [6] s shown. It s clearly observed a reducton of all the search wndows n our approach. These parameters have been used to evaluate the performance of the QRS segmentaton method on the TS2.

4 Table III shows senstvty, PPV, µdj and σdj of the beat detector and the WT segmentaton method, before and after the optmzaton process. Senstvty and PPV were obtaned by usng a 0 ms search wndow. The Table shows an mprovement n the detecton of the QRSon, QRSoff and Isop, and n the detecton of the FP exceptng σdj. Performance results are comparable to those reported n the lterature by usng standard adult's ECG databases [4], [5]. C. QRS complex analyss Table IV shows the wµ, wσ, µad and σad for all tme seres RR, normalzed R Amp (NR Amp ), and QRS Dur. Results for RR and QRS Dur show the hghest values for T3 (bradycarda event), followed T2, and the lowest values for T. The lowest value of the wµ for NR Amp s obtaned for T3 (as well as the hghest value for the wσ). A dmnuton s observed n the wµ from T to T3 as well as an ncrease n the wσ from T to T3. For all the tme seres, hgher values of the µad and the σad are obtaned between T and T3. Sgnfcant dfferences between ntervals T and T3 were observed for the RR tme seres (p<0.000). Addtonally, sgnfcant varatons were observed for all tme seres when comparng T-T2 vs. T-T3 (p<0.0005) and T-T3 vs. T-T4 (p<0.05). Tme seres RR, normalzed R Amp, QRS Dur, and an ECG segment wth QRS segmentaton are llustrated n fgure. Changes n the R-wave ampltude are clearly observed n fgure (c), related to the bradycarda epsode shown n the RR tme seres n fgure (a). nserm , verson - 0 Dec 2009 IV. CONCLUSION Ths paper presents the adaptaton of a beat detector and a WT segmentaton method, to the preterm newborn's ECG. Optmal parameters found by usng evolutonary algorthms have mproved the performance of both methods. By analyzng the RR, R-wave ampltude, and QRS complex duraton from 50 ECG segments from 27 preterm nfants, t was observed a statstcal sgnfcant modfcaton n the ampltude of the R-wave and n the duraton of the QRS complex, assocated wth the onset of the apnea-bradycarda epsodes. These fndngs show the potental beneft of a multvarate approach to early apnea-bradycarda detecton and characterzaton. REFERENCES [] A. Janver, M. Khary, A. Kokkots, C. Cormer, D. Messmer, and K.J. Barrngton, Apnea s assocated wth neurodevelopmental mparment n very low brth weght nfants, J. Pernatol., vol. 24, no. 2, pp , Dec [2] T.M. Bard, Clncal correlates, natural hstory and outcome of neonatal apnea, Semn. Neonatol., vol. 9, no. 3, pp , June [3] R. Pchardo, J.S. Adam, E. Rosow, and J. Bronzno, Vbrotactle stmulaton system to treat apnea of prematurty, Bomed. Instrum. Technol., vol. 37, no., pp , Jan [4] J. Pan, and W.J. Tompkns, A real-tme QRS detecton algorthm, IEEE Trans. Bomed. Eng., vol 32, no. 3, pp , Mar [5] J. Dumont, A. Hernández, and G. Carrault, Improvng ECG Beats Delneaton Wth an Evolutonary Optmzaton Process, IEEE Trans. Bomed. Eng., to be publshed. [6] A. Beuchée, Intérêt de l analyse de la varablté du rythme cardaque en néonataloge comportement des systèmes de régulaton cardovasculare dans le syndrome apnée/bradycarde du nouveau-né, Ph.D. Thess, Medcne School. Rennes Unversty, Rennes, France, [7] C.F. Poets, V.A. Stebbens, M.P. Samuels, and D.P. Southall, The relatonshp between bradycarda, apnea, and hypoxema n preterm nfants,.pedatr. Res., vol. 34, no. 2, pp , 993. [8] A. Smrdel, and F. Jager, Automated detecton of transent ST-segment epsodes n 24 h electrocardograms, Med. Bol. Eng. Comput., vol. 42, no. 3, pp , May [9] A.E. Eben, and J.E. Smth, Introducton to Evolutonary Computng, Berln: Sprnger-Verlag, 2003, ch.3. [0] T. Bäck, and M. Schütz, Intellgent mutaton rate control n canoncal genetc algorthms, n Proc. 9 th Intern. Symp. Method. Intellg. Systems, 996, pp TABLE I PARAMETERS OF THE BEAT DETECTION METHOD AND ITS VALUES BEFORE AND AFTER THE OPTIMIZATION PROCESS Parameter Before After Unts fc Low Hz fc Hgh Hz T MWI ms T Buff ms NP 5 2 Peaks λ α β δ T Refr ms T Peak ms T RRlm ms

5 TABLE II SEARCH WINDOWS AND THRESHOLDS FOR A RR INTERVAL OF 400 MS, AND THE PARAMETERS USED BY DUMONT ET AL., SMRDEL AND JAGER Parameter Our approach Dumont et al. Smrde, and Jager T R ms 8 ms T R ms ms T Qlm 40.2 ms 88 ms T Slm 46.8 ms 54 ms γ QRSpre γ QRSpost ξ QRSon ξ QRSon ξ QRSoff ξ QRSoff T PQ ms 08 ms T Iso 5.96 ms 20 ms nserm , verson - 0 Dec 2009 TABLE III BEAT DETECTION AND QRS SEGMENTATION PERFORMANCE, BEFORE AND AFTER THE OPTIMIZATION PROCESS S (%) PPV (%) µdj (ms) σdj (ms) Crtera FP R-wave QRSon QRSoff ISOp Bef Aft Bef Aft Bef Aft Bef Aft TABLE IV Wµ, Wσ, µad AND σad FOR TIME SERIES RR, NR AMP, AND QRS DUR Crtera RR (ms) NR Amp QRS Dur (ms) (wµ±wσ) T ± ± ±5.88 ( wµ±wσ) T ± ± ±6.22 (wµ±wσ) T3 72.0± ± ±5.06 (wµ±wσ) T ± ± ±7.08 (µad±σad) T-T2.26± ± ±0.7 (µad±σad) T-T ± ± ±.66 (µad±σad) T-T4 3.93± ± ±2.7

6 (a) (b) ms 500 T T2 T3 T ms (c) (d) x 0-4 V Samples Seconds Fg.. (a) RR, (b) QRS duraton, (c) Normalzed R-wave ampltude and (d) typcal QRS complex segmentaton results. In (a)-(c), the vertcal dashed lnes delmt the ntervals T-T4, whereas n (d), the vertcal dashed lnes show the automatc QRS segmentaton (QRSon, R-wave, and QRSoff) nserm , verson - 0 Dec 2009