Methodological issues in the spectral analysis of the heart rate variability: Application in patients with epilepsy

Stavrinou, Maria L.; Sakellaropoulos, George; Trachani, Eftichia; Sirrou, Vassiliki; Polychronopoulos, Panayiotis; Nikiforidis, George; Chroni, Elisabeth

doi:10.1016/j.bspc.2014.03.002

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Short-term HRV analysis is useful for focusing on the effects of the interaction between sympathetic and parasympathetic branches of the ANS and respiration sinus arrhythmia (RSA) rather than the effect of general circadian periodicity [ 36 ]. Short-term HRV analysis also can be used in epileptic patients to investigate cardiac alterations around ictal onsets [ 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

You

Cho

et al. 2021

Medicina

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Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey–Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.

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Section: Introductionmentioning

confidence: 99%

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

You

Cho

et al. 2021

Medicina

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“…The least square approach has improvements in the issues of spectral line splitting and the bias in the positioning of spectral peaks, but is less stable than Burg's algorithm (12, 13, 15). Generally, they obtain similar results in most situations (13, 16), but Burg's algorithm is a more stable approach and is preferable among the three methods (13, 15).…”

Section: Commonly Used Spectral Hrv Analysis Methodsmentioning

confidence: 99%

Spectral Analysis of Heart Rate Variability: Time Window Matters

2019

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Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1–24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis.

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Section: Data Segmentationmentioning

confidence: 99%

“…6) Methods for Spectral Estimation: Generally, there are two types of approaches that can be used to estimate the spectral of HRV data, i.e., non-parametric and parametric methods [33], [162]. In the non-parametric method, the PSD can be calculated directly from the signal data, and there are two widely used non-parametric methods; (i) The first type of nonparametric methods uses the fast Fourier transform (FFT) to calculate the spectral power; (ii) The second type of nonparametric methods is based on Welch's periodogram [33], the method uses a window function (e.g., Hamming window) to calculate periodogram for each data segment, and then computes the averaged periodogram for the data [162]. In the parametric method, the PSD is calculated from the frequency response of the transfer function of a linear system [33], and there are also two types of parametric methods; (i) The first parametric approach is the Yule-Walker method, it uses the lagged-product autocorrelation to estimate the parameters of AR model [163]; (ii) The second parametric approach is the Burg method, which fits the AR model by minimizing the forward and backward prediction errors [33].…”

Section: Data Segmentationmentioning

confidence: 99%

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Lü

Zhu

Morelli

et al. 2024

IEEE Rev. Biomed. Eng.

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Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

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Methodological issues in the spectral analysis of the heart rate variability: Application in patients with epilepsy

Cited by 9 publications

References 31 publications

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Spectral Analysis of Heart Rate Variability: Time Window Matters

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

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