Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

Estévez‐Báez, Mario; Machado, Calixto; García-Sánchez, Benjamín; Rodrı́guez, Valia; Alvarez-Santana, Reynaldo; Leisman, Gerry; Carrera, José Mario Estévez; Schiavi, Adam; Montes-Brown, Julio; Arrufat-Pié, Eduardo

doi:10.1080/02699052.2018.1553312

Cited by 22 publications

(19 citation statements)

References 126 publications

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“…Recently, we have shown that the use of the HMS applied to the heart rate variability analysis can outperform the traditional FFT method for the assessment of patients with cardiac autonomic neuropathy (Estevez-Baez et al 2018) and in patients in coma (Estevez-Baez et al 2019). The results shown in this study strongly support its introduction as an alternative to the traditional FFT spectrum for the spectral analysis of the EEG considering that the Hilbert-Huang method has been developed for the analysis of nonlinear and non-stationary signals.…”

Section: Discussionsupporting

confidence: 57%

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Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

Arrufat-Pié¹,

Jme

et al. 2020

Preprint

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The fast Fourier transform (FFT), has been the main tool for the EEG spectral analysis (SPA). However, as the EEG dynamics shows nonlinear and non-stationary behavior, results using the FFT approach may result meaningless. A novel method has been developed for the analysis of nonlinear and non-stationary signals known as the Hilbert-Huang transform method. In this study we describe and compare the spectral analyses of the EEG using the traditional FFT approach with those calculated with the Hilbert marginal spectra (HMS) after decomposition of the EEG with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-seconds EEG recorded from 19 leads of 47 healthy volunteers were studied. Although the spectral indices calculated for the explored EEG bands showed significant statistical differences for different leads and bands, a detailed analysis showed that for practical purposes both methods performed substantially similar. The HMS showed a reduction of the alpha activity (-5.64%), with increment in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, and also an increment in the theta band (+2.14%), and in the delta (+0.45%) band, and vice versa for the FFT method. For the weighted mean frequencies insignificant mean differences (lower than 1Hz) were observed between both methods for the delta, theta, alpha, beta-1 and beta-2 bands, and only for the gamma band values for the HMS were 3 Hz higher than with the FFT method. The HMS may be considered a good alternative for the SPA of the EEG when nonlinearity or non-stationarity may be present.

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Section: Discussionsupporting

confidence: 57%

“…where P(t) is the instantaneous energy power, θ(t) is the instantaneous phase, and ω(t) is the instantaneous frequency (Estevez-Baez et al 2019;Estevez-Baez et al 2018). The instantaneous values of frequency and power for the EEG segments of 60 seconds were used for the calculation of the HMS.…”

Section: Hilbert Marginal Spectrum (Hms)mentioning

confidence: 99%

Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

Arrufat-Pié¹,

Jme

et al. 2020

Preprint

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“…[75][76][77] It has been widely supported that that lesions in the medulla can cause respiratory dysfunction, like in congenital central hypoventilation syndrome, 78 or severe brain injury, 79 which results in damage to medullar respiratory control function. 35,[80][81][82][83][84] It is extremely important to remark that the brainstem has been severely infected by SARS-CoV34, 35 or MERS-CoV. It has been reported that viral antigens have been detected in the brainstem, where the infected regions included the nucleus of the solitary tract and nucleus ambiguous.…”

Section: Brainstem Dysfunction In Sars-cov-2 Infection As a Potentialmentioning

confidence: 99%

Brainstem Dysfunction in SARS-COV2 Infection Can Be a Potential Cause of Respiratory Distress

Machado¹,

Gutierrez²

2020

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Covid-19 pandemic has captivated scientists to investigate if this new disease can affect the central nervous system (CNS). The most challenging symptoms of Covid-19 are related to respiratory distress, and most patients admitted in intensive care units cannot breathe by their own. Therefore, a crucial question is if respiratory distress can be partially explained by the CNS affection. SARS-Cov-2 is a beta-coronavirus that shares high similarities with SARS-CoV. The infection of SARS‐CoV has been reported in the brains from both patients and experimental animals, where the brainstem was heavily infected. Those coronaviruses have been able to invade the brainstem via a synapse‐connected route to the medullary respiratory center, where the infected regions included the nucleus of the solitary tract and nucleus ambiguous. The vagal afferent nerves from receptors in the lung communicate with the medulla and pons respiratory control centers to coordinate inspiration and expiration. This suggests that neuroinvasion of SARS‐CoV‐2 might play a role in the acute respiratory failure of Covid-19. Therefore, acute respiratory distress in Covid-19 can be partially explained by brainstem dysfunction, suggesting the needs of more specific and aggressive treatments with the direct participation of neurologists and neurointensivists.

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“…where P(t) is the instantaneous energy power and ω(t) is the instantaneous frequency (Estevez-Baez et al 2019;Estevez-Baez et al 2018).…”

Section: Estimation Of the Psd Of The Imfs Using The Hht Methods And Smentioning

confidence: 99%

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

Arrufat-Pié¹,

Estévez‐Báez²,

Estévez‐Carreras³

et al. 2020

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Considering the properties of the empirical mode decomposition to extract from a signal its natural oscillatory components known as intrinsic mode functions (IMFs), the spectral analysis of these IMFs could provide a novel alternative for the quantitative EEG analysis without a priori establish more or less arbitrary band limits. This approach has begun to be used in the last years for studies of EEG records of patients included in database repositories or including a low number of individuals or of limited EEG leads, but a detailed study in healthy humans has not yet been reported. Therefore, in this study the aims were to explore and describe the main spectral indices of the IMFs of the EEG in healthy humans using a method based on the FFT and another on the Hilbert-Huang transform (HHT). The EEG of 34 healthy volunteers was recorded and decomposed using a recently developed multivariate empirical mode decomposition algorithm. Extracted IMFs were submitted to spectral analysis with, and the results were compared with an ANOVA test. The first six decomposed IMFs from the EEG showed frequency values in the range of the classical bands of the EEG (1.5 to 56 Hz). Both methods showed in general similar results for mean weighted frequencies and estimations of power spectral density, although the HHT is recommended because of its better frequency resolution. It was shown the presence of the mode-mixing problem producing a slight overlapping of spectral frequencies mainly between the IMF3 and IMF4 modes.

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Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

Cited by 22 publications

References 126 publications

Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

Brainstem Dysfunction in SARS-COV2 Infection Can Be a Potential Cause of Respiratory Distress

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

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