On the risk of aortic valve replacement surgery assessed by heart rate variability parameters

Żebrowski, Jan J.; Kowalik, Ilona; Orłowska−Baranowska, Ewa; Andrzejewska, Małgorzata; Baranowski, Rafał; Gieraltowski, Jan

doi:10.1088/0967-3334/36/1/163

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…Even if the results were promising, particularly in the field of heart rate variability, it is questioned whether a monofractal DFA estimating one or two coefficients may improve health assessment and risk stratification compared to nonfractal methods (Sassi et al, 2015). Therefore, the more advanced research in this field is investigating how DFA can be enhanced by jointly depicting the multifractal and multiscale nature of the time series in order to model pathophysiological mechanisms more completely (Zebrowski et al, 2015; Solinski et al, 2016).…”

Section: Summary Of Advantages and Limitations And Conclusionmentioning

confidence: 99%

A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

Castiglioni

Faini

2019

Front. Physiol.

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Detrended fluctuation analysis (DFA) is a popular tool in physiological and medical studies for estimating the self-similarity coefficient, α, of time series. Recent researches extended its use for evaluating multifractality (where α is a function of the multifractal parameter q ) at different scales n . In this way, the multifractal-multiscale DFA provides a bidimensional surface α( q,n ) to quantify the level of multifractality at each scale separately. We recently showed that scale resolution and estimation variability of α( q,n ) can be improved at each scale n by splitting the series into maximally overlapped blocks. This, however, increases the computational load making DFA estimations unfeasible in most applications. Our aim is to provide a DFA algorithm sufficiently fast to evaluate the multifractal DFA with maximally overlapped blocks even on long time series, as usually recorded in physiological or clinical settings, therefore improving the quality of the α( q,n ) estimate. For this aim, we revise the analytic formulas for multifractal DFA with first- and second-order detrending polynomials (i.e., DFA 1 and DFA 2 ) and propose a faster algorithm than the currently available codes. Applying it on synthesized fractal/multifractal series we demonstrate its numerical stability and a computational time about 1% that required by traditional codes. Analyzing long physiological signals (heart-rate tachograms from a 24-h Holter recording and electroencephalographic traces from a sleep study), we illustrate its capability to provide high-resolution α( q,n ) surfaces that better describe the multifractal/multiscale properties of time series in physiology. The proposed fast algorithm might, therefore, make it easier deriving richer information on the complex dynamics of clinical signals, possibly improving risk stratification or the assessment of medical interventions and rehabilitation protocols.

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Section: Summary Of Advantages and Limitations And Conclusionmentioning

confidence: 99%

A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

Castiglioni

Faini

2019

Front. Physiol.

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“…Filipovic et al [ 74 ] found a higher risk of death 30 days after major non-cardiac surgery in patients with preOP LF/HF < 2.0. Zebrowski et al [ 75 ] did not find significant differences in LF, HF, and LF/HF between patients who died within 30 days after aortic valve surgery, and those who survived. They did however find higher rMSSD (p = 0.0054) in the group that died, along with differences in other time domain analyses (lower Guzik’s, Porta’s, and Ehler’s index and higher index D) [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

“…Zebrowski et al [ 75 ] did not find significant differences in LF, HF, and LF/HF between patients who died within 30 days after aortic valve surgery, and those who survived. They did however find higher rMSSD (p = 0.0054) in the group that died, along with differences in other time domain analyses (lower Guzik’s, Porta’s, and Ehler’s index and higher index D) [ 75 ]. The two studies did not specify when in the preOP course HRV measurements were taken.…”

Section: Resultsmentioning

confidence: 99%

Preoperative heart rate variability as a predictor of perioperative outcomes: a systematic review without meta-analysis

Frandsen

Mehlsen

Foss

et al. 2022

J Clin Monit Comput

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Heart rate variability (HRV) is a predictor of mortality and morbidity after non-lethal cardiac ischemia, but the relation between preoperatively measured HRV and intra- and postoperative complications is sparsely studied and most recently reviewed in 2007. We, therefore, reviewed the literature regarding HRV as a predictor for intra- and postoperative complications and outcomes. We carried out a systematic review without meta-analysis. A PICO model was set up, and we searched PubMed, EMBASE, and CENTRAL. The screening was done by one author, but all authors performed detailed review of the included studies. We present data from studies on intraoperative and postoperative complications, which were too heterogeneous to warrant formal meta-analysis, and we provide a pragmatic review of HRV indices to facilitate understanding our findings. The review was registered in PROSPERO (CRD42021230641). We screened 2337 records for eligibility. 131 records went on to full-text assessment, 63 were included. In frequency analysis of HRV, low frequency to high frequency ratio could be a predictor for intraoperative hypotension in spinal anesthesia and lower total power could possibly predict intraoperative hypotension under general anesthesia. Detrended fluctuation analysis of HRV is a promising candidate for predicting postoperative atrial fibrillation. This updated review of the relation between preoperative HRV and surgical outcome suggests a clinically relevant role of HRV but calls for high quality studies due to methodological heterogeneity in the current literature. Areas for future research are suggested.

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“…KLD was calculated only for the nighttime recordings of the heart rhythm. Because of the different time for every patient for going to sleep, for each case the period of observation was selected using the average RR value over time (Gierałtowski et al, 2012;Żebrowski et al, 2015). These records were also analyzed with the use of windows (the tested signal was divided into adjacent, non-overlapping windows).…”

Section: Methodsmentioning

confidence: 99%

Assessment of time irreversibility in a time series using visibility graphs

Andrzejewska

Żebrowski

Rams

et al. 2022

Front. Netw. Physiol.

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In this paper, we studied the time-domain irreversibility of time series, which is a fundamental property of systems in a nonequilibrium state. We analyzed a subgroup of the databases provided by University of Rochester, namely from the THEW Project. Our data consists of LQTS (Long QT Syndrome) patients and healthy persons. LQTS may be associated with an increased risk of sudden cardiac death (SCD), which is still a big clinical problem. ECG-based artificial intelligence methods can identify sudden cardiac death with a high accuracy. It follows that heart rate variability contains information about the possibility of SCD, which may be extracted, provided that appropriate methods are developed for this purpose. Our aim was to assess the complexity of both groups using visibility graph (VG) methods. Multivariate analysis of connection patterns of graphs built from time series was performed using multiplex visibility graph methods. For univariate time series, time irreversibility of the ECG interval QT of patients with LQTS was lower than for the healthy. However, we did not observe statistically significant difference in the comparison of RR intervals time series of the two groups studied. The connection patterns retrieved from multiplex VGs have more similarity with each other in the case of LQTS patients. This observation may be used to develop better methods for SCD risk stratification.

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On the risk of aortic valve replacement surgery assessed by heart rate variability parameters

Cited by 11 publications

References 25 publications

A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

Preoperative heart rate variability as a predictor of perioperative outcomes: a systematic review without meta-analysis

Assessment of time irreversibility in a time series using visibility graphs

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