1 Decreased Scale-specific Heart Rate Variability after Multiresolution Wavelet Analysis predicts Sudden Cardiac Death in Heart Failure Patients Petros Arsenos MD a , Konstantinos Gatzoulis MD a, George Manis DIPL-ENG b, Theodoros Gialernios MD a, Polychronis Dilaveris MD, FESC a, Dimitrios Tsiachris MD a, Stefanos Archontakis MD a, Efstathios Kartsagoulis MD a, Dimitrios Mytas MD c, Christodoulos Stefanadis MD, FESC, FACC a. a. First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece. b. Department of Computer Science, University of Ioannina, Ioannina, Greece. c. Department of Cardiology, Sismanogleio Hospital, Marousi, Greece Conflict of interest: none Financial support: none Address for correspondence: Petros Arsenos, MD Chalkidos Athinon 12 Avlonas Attikis, 190 11, Greece Tel & Fax ++30 2295042807 Mail: [email protected] Keywords: Wavelets, Scale-specific Heart Rate Variability, Holter, Heart Failure, Sudden Cardiac Death. 2 In the present study, we aimed to assess whether Multiresolution Wavelet Analysis (MWA) [1] of Heart Rate Variability (HRV) [2] carries significant prognostic information independently from other well established stratifiers in the field of Sudden Cardiac Death (SCD) prediction [3,4]. All 231 HF patients gave informed consent and the study was approved by our institution’s Ethics Committee. They underwent physical examination, personal and family history, medications recording, chest X Ray, blood and biochemical tests, 12 leads-ECG (25mm/sec, MAC 5000, GE Marquette Medical, Milwaukee, USA), ECHO (SONOS 5500, Hewlett Packard, Andover, Massachusetts, USA), Signal Averaged ECG (SAECG/ MAC 5000 GE Medical, Milwaukee,USA) and Holter Monitoring (HM/Spider View-1000Hz and SyneScope 3.10 software, Sorin Group, Ela Medical, Clamart, France). Patients with NSVT episodes (n=87) were further risk stratified by Electrophysiological Study (EPS). The patients with clinical (n=12) or inducible Ventricular Tachycardia/Ventricular Fibrillation (VT/VF) on EPS (n=50) received an ICD (n=62). The sample size was divided into the high risk (n=46) and the low risk (n=185) groups, according to three SCD events/surrogates: 1.clinical VT/VF (n=12), 2. ICD’s appropriate activation (n=22), 3. confirmed SCD (n=12). MWA was performed using Matlab software [5]. The Haar wavelet was used and the final index σwav was extracted as the standard deviation of the detailed coefficients of scale 8. Statistical results are presented as Hazard Ratios (HR) and the 95% confidence intervals (C.I.). A p value <0.05 was considered statistically significant. Arrhythmic events rate was tabulated with Kaplan-Meier curve. STATA 8.0 software (Stata Corporation 2003, Texas, USA) was used for all statistical calculations. Baseline clinical characteristics are presented in Table 1. Univariate analysis searched with Log rank test (table 2 ) and a Cox proportional hazard survival model was used to determine whether σwav predicted SCD independently from other risk stratifiers. The model was built on LVEF, fQRS from SAECG, Heart rate, VPBs>10/hour, NSVT episodes>1/24hours, mean 3 QTc, age, gender, and σwav (table 3). In this model σwav was a statistically important arrhythmia predictor, with Hazard Ratio of 0.991 (p<0.001, 95% CI: 0.987 - 0.996). Furthermore σwav values were dichotomized at 25th percentile (cutoff point=181) for tabulating Kaplan Meier arrhythmia events (Figure 1). The σwav (continuous values) was replaced by cutoff σwav<181 at the previous multivariable Cox model. The analysis revealed that patients with σwav values bellow 181 had Hazard Ratio 2.526 (p=0.01), 95% CI: 1.2135.259 for SCD surrogate end points.(table 3).The most interesting finding of our study was that in heartbeat time series of SCD high risk patients was indentified a specific scale with decreased HRV. That means that HRV reflected by σwav at segments with a periodicity of 256 RR duration was found diminished in SCD patients. Thurner, first analysed heart beat timeseries with MWA method and reported correct classification of a small number of ECG signals retrieved from two different groups: a heart failure patients group and a group of healthy subjects [1]. These results were confirmed by Ashkenazy at 1998 [6] and again at 2001 in a sample of 116 patients [7]. Wavelets were used to quantify HRV and assess its instantaneous changes during atropine and propanolol administration [8] , during four classical autonomic tests [9] , for studying HRV during myocardial ischaemia [10] and for studying reperfusion-depended autonomic changes during thrombolysis [11]. In our study the scale which was proved most significant was scale 8, corresponding to 256 beat intervals and not scales 4 and 5 as in Thurners’ experiments (scale 4 was also highly predictive in our analysis). The incompatibility in predictive scales reported by Thurner and our team is abolished after a deeper study of Thurner’s article. He demonstrated that scales m=4 and m=5 (16-32 heartbeat intervals) were proved important for the separation of normal from heart failure patients. This was the main conclusion of his article, which was also reflected in its title. The separation between controls and the lone patient experiencing SCD that was included into the total patient’s sample has not been discussed. After a deeper look at fig.2 page 1546 of this article, where σwav is presented as a function of scale [1], it is obvious that the σwav values for the SCD subject, decreases significantly and progressively from scales 6 4 and 7 to scale 8. This finding is in accordance with our results. In fact both studies, Thurner’s and ours, demonstrate that HF patients with increased risk for SCD exhibit a reduced scale specific (m=8) σwav variability after Haar wavelet analysis in comparison to their control group. The next question that appears is why in the case of SCD candidates the HRV is affected and reduced in this specific scale (m=8 corresponding to 256 RR duration segments), an issue for future investigation. From our study it is obvious that σwav/scale 8 carries important predictive information and from the electrophysiological point of view σwav represents variability. Since the signal fluctuates in time, so too does the sequence of wavelet coefficients at any given scale; a natural measure for this variability is the wavelet coefficient standard deviation, as a function of scale [1]. In our univariate analysis the σwav index outperformed the conventional SDNN in SCD prediction. It is possible that the σwav/scale 8 index carried more crucial information for the variability status than the general statistical index SDNN calculated in the time domain. If this signal is been analyzed with a general statistical time domain method, for example the SDNN for the entire 24 hour timeseries, this sensitive information concealed in scale 8 and representing diminished HRV in segments with 256 RR duration periodicity is been lost. Thus, MWA extracts different information than that extracted from SDNN. From the other point of view, the scale 8 and the 256 RR interval duration periodicity (our patients presented mean heart rate 70 beats/min and mean RR=857msec) corresponds to 0.004Hz and belongs in the Very Low Frequencies band after signal FFT analysis (VLF: 0.0033Hz-0.04Hz). According our study’s log rank results presenting in table 2, σwav index outperforms the rest HRV indices analyzed in the frequency domain after FFT. This may happens because Haar wavelet and the wavelet mother function fits better the shape of the analyzed signal, allowing a better quantitative measurement [8]. ACKNOWLEDGEMENT The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. [12] 5 REFERENCES 1. Thurner S, Feurstein MC, Teich MC. Multiresolution Wavelet Analysis of Heartbeat Intervals Discriminates Healthy Patients from Those with Cardiac Pathology. Physical Review Letters 1998;80:1544-1547. 2. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart Rate Variability. Standards of measurement, physiological interpretation, and clinical use. European Heart Journal 1996;17:354381. 3. Goldberger JJ, Cain ME, Hohnloser SH et al. Scientific Statement on Noninvasive Risk Stratification Techniques for Identifying Patients at Risk for Sudden Cardiac Death. Expert Consensus Document. JACC 2008;52:1179-1199. 4. Arsenos P, Gatzoulis K, Dilaveris P, Gialernios T, Sideris S, Lazaros G, Archontakis S, Tsiachris D, Kartsagoulis E, Stefanadis C. The rate-corrected QT interval calculated from 24-hour Holter recordings may serve as a significant arrhythmia risk stratifier in heart failure patients. Int J Cardiol 2011;147:321-323. 5. The Mathworks, Natick, Massachusetts, U.S.A., www.mathworks.com. 6. Ashkenazy Y, Lewkowicz M, Levitan J, Moelgaard H, Bloch Thomsen PE, Saermark K. Discrimination of the Healthy and Sick Cardiac Autonomic Nervous System by a New Wavelet Analysis of Heartbeat Intervals. Fractals 1998; (6) 3:197-203 7. Ashkenazy Y, Lewkiwicz M,Levitan J,Havlin S,Saermark K,Moelgaard H,Bloch Thomsen PE, Moller M, Hintze U, Huikuri HV.Scale-specific and scale-independent measures of heart rate variability as risk indicators.Europhysics letters 2001;53(6):709715. 8. Pichot V, Gaspoz J-M, Molliex S, Antoniadis A, Busso T, Roche F, Costes F, 6 Quintin L,Lacour J-R,Barthelemy J-C. Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. J Appl Physiol 1999;86:1081- 1091. 9. Ducla-Soares JL, Santos-Bento M, Laranjo S, Andrade A, Ducla-Soares E, Boto JP, Silva –Carvalho L,Rocha I. Wavelet analysis of autonomic outflow of normal subjects on head-up tilt, cold pressor test, Valsava manoeuvre and deep breathing. Exp Physiol 2007; 92.4:677-686. 10. Gamero LG, Vila J, Palacios F. Wavelet transform analysis of heart rate variability during myocardial ischaemia. Med Biol Eng Comput 2002;40:72-78. 11. Toledo E, Gurevitz O, Hod H, Eldar M, Akselrod S. Wavelet analysis of instantaneous heart rate: a study of autonomic control during thrombolysis. Am J Physiol Regul Integr Comp Physiolo 2003;284:R1079-R1091. 12. Shewan LJ, Coats AJ. Ethics in the authorship and publishing of scientific articles . Intern Journal of Cardiology 2010;144:1-2 7 Tables. Table 1. Baseline patients characteristics. Characteristics Age Male sex Total (n=231) SCD + (n=46) SCD - (n=185) p value (years) (%) 65.2±13.1 194 (84) 63.0±13.6 40(87) 65.9±13.0 154(83) NS NS CAD STEMI Non STEMI CABG PTCA DCMP Hypertrophic Diabetes Hypertension (%) (%) (%) (%) (%) (%) (%) (%) (%) 184(80) 127(55) 20(9) 65(28) 65(28) 46( 20) 1(0.5) 81(35) 141(61) 37( 80) 28( 61) 5(11) 15(33) 8(17) 8(17) 1(2) 14(30) 23(50) 147(79) 99(54) 15(8) 50(27) 57(31) 38(21) 0(0) 67(36) 118 ( 64) NS NS NS NS 0.05 NS 0.04 NS NS LVEF ΝΥΗΑ (%) class 32.4±10.3 2.3±0.5 27.5±9.2 2.5±0.5 33.7±10.2 2.2±0.5 0.003 0.006 Ht Urea Creatinine Sodium Potassium (%) mg/dl mg/dl meq/L meq/L 41±5 55±35 1.3±0.5 138±3.6 4.2±0.5 40±5 51±24 1.2±0.3 138±3.1 4.3±0.5 41±5 56±37 1.3±0.5 138±3.7 4.2±0.5 NS NS NS NS NS 30(65) 23(50) 21(46) 6(13) 26(57) 7(15) 7(15) 17(37) 31(67) 5(11) 12(26) 13(28) 23(50) 4(9) 124(67) 65(35) 100(54) 39(21) 117(63) 74(40) 21(11) 73(39) 108(58) 11(6) 33(18) 33(18) 118(64) 21(11) Medications Bblockers Carbedilol ACEI ARBs Acetyl sal Clopidogrel Coumarine Nitriate Diuretics Digoxin Spironolactone Amiodarone Statins CCBs (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 154(67) 88(38) 121(52) 45(19) 143(62) 81(35) 28(12) 90(39) 139(60) 16(7) 45(19) 46(20) 141(61) 25(11) NS NS NS NS NS 0.004 NS NS NS NS NS NS NS NS ABBREVIATIONS: ACEi: angiotensin-converting enzyme inhibitor, ARBs: angiotensin-II receptor blocker, CABG: coronary artery bypass graft surgery, CAD: coronary artery disease, CCBs: calcium channel blockers, DCMP: dilated cardiomyopathy, LVEF: left ventricular ejection fraction, NYHA: New York Heart Association class, PCI: Primary percutaneous coronary intervention, Non STEMI: myocardial infarction without ST elevation , STEMI: myocardial infarction with ST elevation, 8 Table 2 . Univariate analysis and the p values from Log rank tests for the SCD predictors Variable p value σ wav 0.0003 LVEF 0.006 NSVT>1/24hour 0.007 Heart rate /24hour 0.01 VPBs>240/24 hour 0.02 SDNN/HRV (ms) 0.04 TP (ms²) 0.008 VLF (ms²) 0.01 LF (ms²) 0.01 HF (ms²) 0.05 fQRS 0.08 QTc 0.09 Age 0.118 ABBREVIATIONS: σwav: scale dependent wavelet-coefficient standard deviation, LVEF: Left ventricular ejection fraction, NSVT>1/24hour: Non sustained ventricular tachycardia episodes more than 1per 24 hour, VPBs>240/24 hour: ventricular premature beats more than 240 per 24 hour, SDNN/HRV: standard deviation normal to normal beat from heart rate variability, TP; total power of variability after fast Fourier transform,VLF: very low frequencies, LF: low frequencies, HF: high frequencies, fQRS: filtered QRS from signal averaged ECG, QTc: rate corrected QT interval derived from Holter. 9 Table 3. Multivariate Cox’s regression analysis of the predictors for the occurrence of SCD Variables Hazard Ratio (95% CI) p value σ wav 0.991 (0.987-0.996) <0.001 WAV<181 (cut off) 2.526 (1.213-5.259) 0.013 NSVT>1/24 hour 1.680 (0.825-3.418) 0.152 (continuous) Full model adjusted for gender, age, LVEF, fQRS, Heart Rate, VPBs>240/24hours, NSVT episodes>1/24 hour, QTc and σwav (continuous / cut off point at 181) ABBREVIATIONS: σwav: scale dependent wavelet-coefficient standard deviation, WAV<181: cut off point of σ wav at value of 181, NSVT>1/24hour: non sustained ventricular tachycardia episodes more than 1per 24 hour, LVEF: left ventricular ejection fraction, fQRS: filtered QRS from signal averaged ECG, VPBs>240/24 hour: ventricular premature beats more than 240/24 hour, QTc: rate corrected QT interval derived from Holter 10 Figure 1 Figure 1: Arrhythmia events curve. Patients with σwav<181 present higher SCD surrogate episodes event rate in comparison to patients with σwav>181. The hazard ratio is 2.526.