Heart rate variability as an autonomic biomarker in ischemic stroke

Buitrago-Ricaurte, Natalia; Cintra, Fátima Dumas; Silva, Gisele Sampaio

doi:10.1590/0004-282x20200087

Cited by 22 publications

(15 citation statements)

References 50 publications

(54 reference statements)

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“…HRV analysis is proving to be crucial for risk stratification and prognosis of various cardiovascular conditions as well as chronic disease classification and progression. In addition, changes in HRV in unrelated diseases such as stroke 60 , dementia 61 , mental illness 62 , renal failure, diabetes, sleep apnea, stress, and pain among others have been observed 5 , 63 , 64 . Noise in the recording, being technical or ectopic beats influence any analysis of the variance in the signal.…”

Section: Discussionmentioning

confidence: 99%

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Saleem

Khandoker

Alkhodari

et al. 2022

Sci Rep

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Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10–5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.

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Section: Discussionmentioning

confidence: 99%

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Saleem

Khandoker

Alkhodari

et al. 2022

Sci Rep

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“…HRV measures the fluctuation of the time intervals between consecutive heartbeats, revealing the dynamic interactions between the sympathetic and parasympathetic functions. HRV has been suggested as a possible biomarker for dysautonomia in conditions such as diabetes mellitus type 2 [14] and ischaemic stroke [15]. Having the advantage of being directly obtained from diagnostic v-PSG, HRV is an inexpensive measure of cardiac dysautonomia compared to examinations such as 123 I-MIBG-scintigraphy [16], and could potentially be an objective measure of autonomic dysfunction in subjects at risk of developing an alpha-synucleinopathy.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

Ventosa¹,

Kulcsárová²,

Mertová³

et al. 2023

Neurol Neurochir Pol.

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Introduction.Nearly 80% of people diagnosed with idiopathic REM sleep behaviour disorder (iRBD) via video-polysomnography (v-PSG) are expected to be in the prodromal stage of an alpha-synucleinopathy. Signs of autonomic dysfunction can appear earlier than motor or cognitive alpha-synucleinopathy symptoms. Heart Rate Variability (HRV) can potentially be an objective measurement of autonomic dysfunction, and furthermore can be obtained directly from v-PSG.Objectives. The aim of this study was to evaluate dysautonomia in iRBD subjects using HRV obtained during different sleep stages and wakefulness from v-PSG. Material and methods.Subjects positively screened by an RBD screening questionnaire (RBD-SQ) underwent v-PSG to diagnose RBD. HRV obtained from v-PSG recordings was correlated to dysautonomia evaluated from a Non-Motor Symptoms Scale (NMSS) questionnaire. Optimal cut-off values of HRV parameters to predict dysautonomia were calculated using receiver operating characteristics (ROC) -area under the curve (AUC) analysis. The effect of confounder variables was predicted with binomial logistic regression and multiple regression analyses.Results. Out of 72 positively screened subjects, 29 subjects were diagnosed as iRBD (mean age 66 ± 7.7 years) by v-PSG. Eighty--three per cent of the iRBD subjects in our cohort were at the time of diagnosis classified as having possible or probable prodromal Parkinson's Disease (pPD) compared to zero subjects being positively screened in the control group. The iRBD-positive subjects showed significant inverse correlations of NMSS score, particularly to log low-frequency (LF) component of HRV during wakefulness: r = -0.59 (p = 0.001). Based on ROC analysis and correlation between NMSS score, log LF during wakefulness (AUC 0.74, cut-off 4.69, sensitivity 91.7%, specificity 64.7%, p = 0.028) was considered as the most accurate predictor of dysautonomia in the iRBD group. Apnoea-hypopnoea index (AHI) negatively predicted dysautonomia in the iRBD group. None of the HRV components was able to predict the presence of iRBD in the full cohort. Age, gender, and PSG variables were significant confounders of HRV prediction.Conclusions. The presented study did not confirm the possibility of using HRV from v-PSG records of patients with iRBD to predict dysautonomia expressed by questionnaire methods. This is probably due to several confounding factors capable of influencing HRV in such a cohort.

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“…An elevated HRV is regarded as the sign of high fitness and adaptability of the heart, while reduced HRV levels are usually associated with various pathological conditions, such as congestive heart failure, diabetic neuropathy, mental disorders, post-traumatic stress syndrome, cancer, etc. [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach in Heart-Rate-Variability Analysis Based on Modified Poincaré Plots

2022

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Heart rate variability as an autonomic biomarker in ischemic stroke

Cited by 22 publications

References 50 publications

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

A Novel Approach in Heart-Rate-Variability Analysis Based on Modified Poincaré Plots

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