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• W2007557380 abstract "In the present study, we aimed to assess whether multiresolution wavelet analysis (MWA) [ [1] Thurner S. Feurstein M.C. Teich M.C. Multiresolution wavelet analysis of heartbeat intervals discriminates healthy patients from those with cardiac pathology. Phys Rev Lett. 1998; 80: 1544-1547 Google Scholar ] of heart rate variability (HRV) [ [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. Eur Heart J. 1996; 17: 354-381 Google Scholar ] carries significant prognostic information independently from other well established stratifiers in the field of sudden cardiac death (SCD) prediction [ 3 Goldberger J.J. Cain M.E. Hohnloser S.H. 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 Google Scholar , 4 Arsenos P. Gatzoulis K. Dilaveris P. et al. 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 Google Scholar ]. 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 (25 mm/s, MAC 5000, GE Marquette Medical, Milwaukee, USA), ECHO (SONOS 5500, Hewlett Packard, Andover, MA, USA), signal averaged ECG (SAECG/MAC 5000 GE Medical, Milwaukee, USA) and Holter Monitoring (HM/Spider View-1000 Hz 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 Mathworks, Natick, Massachusetts, U.S.A. www.mathworks.com Google Scholar ]. 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 (CI). 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, TX, 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 adjusted for LVEF, fQRS from SAECG, heart rate, VPBs >10/hour, NSVT episodes >1/24 hours, mean QTc, age, gender, and σwav (Table 3). In this model σwav was a statistically significant 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 (Fig. 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 below 181 had hazard ratio 2.526 (p=0.01), 95% CI: 1.213–5.259 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 analyzed heart beat time series 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] Thurner S. Feurstein M.C. Teich M.C. Multiresolution wavelet analysis of heartbeat intervals discriminates healthy patients from those with cardiac pathology. Phys Rev Lett. 1998; 80: 1544-1547 Google Scholar ]. These results were confirmed by Ashkenazy at 1998 [ [6] Ashkenazy Y. Lewkowicz M. Levitan J. Moelgaard H. Bloch Thomsen P.E. Saermark K. Discrimination of the healthy and sick cardiac autonomic nervous system by a new wavelet analysis of heartbeat intervals. Fractals. 1998; 6: 197-203 Google Scholar ] and again at 2001 in a sample of 116 patients [ [7] Ashkenazy Y. Lewkiwicz M. Levitan J. et al. Scale-specific and scale-independent measures of heart rate variability as risk indicators. Europhysics lett. 2001; 53: 709-715 Google Scholar ]. Wavelets were used to quantify HRV and assess its instantaneous changes during atropine and propanolol administration [ [8] Pichot V. Gaspoz J.-M. Molliex S. et al. Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. J Appl Physiol. 1999; 86: 1081-1091 Google Scholar ], during four classical autonomic tests [ [9] Ducla-Soares J.L. Santos-Bento M. Laranjo S. et al. Wavelet analysis of autonomic outflow of normal subjects on head-up tilt, cold pressor test, Valsava manoeuvre and deep breathing. Exp Physiol. 2007; 92: 677-686 Google Scholar ], for studying HRV during myocardial ischemia [ [10] Gamero L.G. Vila J. Palacios F. Wavelet transform analysis of heart rate variability during myocardial ischaemia. Med Biol Eng Comput. 2002; 40: 72-78 Google Scholar ] and for studying reperfusion-dependent autonomic changes during thrombolysis [ [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 Physiol. 2003; 284: R1079-R1091 Google Scholar ]. 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 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] Thurner S. Feurstein M.C. Teich M.C. Multiresolution wavelet analysis of heartbeat intervals discriminates healthy patients from those with cardiac pathology. Phys Rev Lett. 1998; 80: 1544-1547 Google Scholar ], it is obvious that the σwav values for the SCD subject decrease significantly and progressively from scales 6 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 the electrophysiological point of view σwav represents variability. Since the signal fluctuates in time, so too does the sequence of wavelet coefficients; a natural measure for this variability is the wavelet coefficient standard deviation, as a function of scale [ [1] Thurner S. Feurstein M.C. Teich M.C. Multiresolution wavelet analysis of heartbeat intervals discriminates healthy patients from those with cardiac pathology. Phys Rev Lett. 1998; 80: 1544-1547 Google Scholar ]. 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 has been analyzed with a general statistical time domain method, for example the SDNN for the entire 24 hour time series, this sensitive information concealed in scale 8 and representing diminished HRV in segments with 256 RR duration periodicity has 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=857 ms) correspond to 0.004 Hz and belong in the Very Low Frequencies band after signal FFT analysis (VLF: 0.0033 Hz–0.04 Hz). According our study's log rank results presented in Table 2, σwav index outperforms the rest HRV indices analyzed in the frequency domain after FFT. This may happen because Haar wavelet and the wavelet mother function fit better the shape of the analyzed signal, allowing a better quantitative measurement [ [8] Pichot V. Gaspoz J.-M. Molliex S. et al. Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. J Appl Physiol. 1999; 86: 1081-1091 Google Scholar ]. 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. Abbreviations: σwav: Scale dependent wavelet-coefficient standard deviation, LVEF: left ventricular ejection fraction, NSVT >1/24 hours: non sustained ventricular tachycardia episodes more than 1 per 24 hours, VPBs >240/24 hours: ventricular premature beats more than 240 per 24 hours, 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. Full model adjusted for gender, age, LVEF, fQRS, heart rate, VPBs >240/24 hours, NSVT episodes >1/24 hours, QTc and σwav (continuous/cut off point at 181). Abbreviations: σwav: scale dependent wavelet-coefficient standard deviation, WAV <181: cut off point of σwav at value of 181, NSVT >1/24 hours: non sustained ventricular tachycardia episodes more than 1 per 24 hours, LVEF: left ventricular ejection fraction, fQRS: filtered QRS from signal averaged ECG, VPBs >240/24 hours: ventricular premature beats more than 240/24 hours, QTc: rate corrected QT interval derived from Holter." @default.
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• W2007557380 title "Decreased scale-specific heart rate variability after multiresolution wavelet analysis predicts sudden cardiac death in heart failure patients" @default.
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• W2007557380 doi "https://doi.org/10.1016/j.ijcard.2011.11.007" @default.
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