Time-frequency analysis of spontaneous pupillary oscillation signals using the Hilbert-Huang transform

Villalobos-Castaldi, Fabiola M.; Ruiz-Pinales, José; Valverde, Nicolás C. Kemper; Flores, Mercedes

doi:10.1016/j.bspc.2016.06.002

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Furthermore, non-linear fluctuations seem to contribute to pupillary unrest (Mesin et al, 2013;Onorati et al, 2015;Schumann et al, 2015;Villalobos-Castaldi et al, 2016). The variety of influencing factors make spontaneous pupil size variations at rest complicated to quantify and to interpret (Usui and Stark, 1982;Mesin et al, 2013;Schumann et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Sympathetic and Parasympathetic Modulation of Pupillary Unrest

et al. 2020

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Pupillary unrest is an established indicator of drowsiness or sleepiness. How sympathetic and parasympathetic activity contribute to pupillary unrest is not entirely unclear. In this study, we investigated 83 young healthy volunteers to assess the relationship of pupillary unrest to other markers of the autonomic nervous system. Sample entropy (SE) and the established pupillary unrest index (PUI) were calculated to characterize pupil size variability. Autonomic indices were derived from heart rate, blood pressure, respiration, and skin conductance. Additionally, we assessed individual levels of calmness, vigilance, and mood. In an independent sample of 26 healthy participants, we stimulated the cardiovagal system by a deep breathing test. PUI was related to parasympathetic cardiac indices and sleepiness. A linear combination of vagal heart rate variability [root mean square of heart beat interval differences (RMSSD)] and skin conductance fluctuations (SCFs) was suited best to explain interindividual variance of PUI. Complexity of pupil diameter (PD) variations correlated to indices of sympathetic skin conductance. Furthermore, we found that spontaneous fluctuations of skin conductance are accompanied by increases of pupil size. In an independent sample, we were able to corroborate the relation of PUI with RMSSD and skin conductance. A slow breathing test enhanced RMSSD and PUI proportionally to each other, while complexity of PD dynamics decreased. Our data suggest that the slow PD oscillations (f < 0.15 Hz) quantified by PUI are related to the parasympathetic modulation. Sympathetic arousal as detected by SCFs is associated to transient pupil size increases that increase non-linear pupillary dynamics.

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Section: Introductionmentioning

confidence: 99%

Sympathetic and Parasympathetic Modulation of Pupillary Unrest

et al. 2020

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“…Hilbert spectral analysis is a signal analysis technique that uses the Hilbert transform to calculate the instantaneous frequency of signals [27].…”

Section: Hilbert Spectrummentioning

confidence: 99%

Empirical Mode Decomposition and Hilbert Spectrum for Abnormality Detection in Normal and Abnormal Walking Transitions

Erfianto

Rizal

Hadiyoso

2023

IJERPH

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Sensor-based human activity recognition (HAR) is a method for observing a person’s activity in an environment. With this method, it is possible to monitor remotely. HAR can analyze a person’s gait, whether normal or abnormal. Some of its applications may use several sensors mounted on the body, but this method tends to be complex and inconvenient. One alternative to wearable sensors is using video. One of the most commonly used HAR platforms is PoseNET. PoseNET is a sophisticated platform that can detect the skeleton and joints of the body, which are then known as joints. However, a method is still needed to process the raw data from PoseNET to detect subject activity. Therefore, this research proposes a way to detect abnormalities in gait using empirical mode decomposition and the Hilbert spectrum and transforming keys-joints, and skeletons from vision-based pose detection into the angular displacement of walking gait patterns (signals). Joint change information is extracted using the Hilbert Huang Transform to study how the subject behaves in the turning position. Furthermore, it is determined whether the transition goes from normal to abnormal subjects by calculating the energy in the time-frequency domain signal. The test results show that during the transition period, the energy of the gait signal tends to be higher than during the walking period.

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“…These methods assume an underlying stationary signal or require an a priori knowledge of the shape of the single basis wave; assumptions that do not well reflect the pupillary dynamics (Onorati et al, 2016). Among the most recent proposed non-linear and non-stationary meeting methods for the analysis of the pupil oscillations, there are the Hilbert Huang Transform, the EMD (Ruiz-Pinales et al, 2016; Villalobos-Castaldi et al, 2016), and the recurrence plots (Mesin et al, 2013, 2014; Monaco et al, 2014). The Hilbert-Huang transform is a frequency domain transformation, with the advantage of maintaining a good temporal and frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Through the EMD, the original signal is split into components with slowly varying amplitude and phase, also known as IMFs. By applying a Hilbert transform to the IMF, instantaneous frequencies are generated as functions of time that give sharp identifications of embedded structures (Barnhart, 2011; Ruiz-Pinales et al, 2016; Villalobos-Castaldi et al, 2016). The RQA consists in taking single physiological measurements, projecting them into multidimensional space by embedding procedures and in identifying correlations that are not apparent in one-dimensional time series.…”

Section: Introductionmentioning

confidence: 99%

A Cross-Recurrence Analysis of the Pupil Size Fluctuations in Steady Scotopic Conditions

et al. 2019

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Pupil size fluctuations during stationary scotopic conditions may convey information about the cortical state activity at rest. An important link between neuronal network state modulation and pupil fluctuations is the cholinergic and noradrenergic neuromodulatory tone, which is active at cortical level and in the peripheral terminals of the autonomic nervous system (ANS). This work aimed at studying the low- and high-frequency coupled oscillators in the autonomic spectrum (0–0.45 Hz) which, reportedly, drive the spontaneous pupillary fluctuations. To assess the interaction between the oscillators, we focused on the patterns of their trajectories in the phase-space. Firstly, the frequency spectrum of the pupil signal was determined by empirical mode decomposition. Secondly, cross-recurrence quantification analysis was used to unfold the non-linear dynamics. The global and local patterns of recurrence of the trajectories were estimated by two parameters: determinism and entropy. An elliptic region in the entropy-determinism plane (95% prediction area) yielded health-related values of entropy and determinism. We hypothesize that the data points inside the ellipse would likely represent balanced activity in the ANS. Interestingly, the Epworth Sleepiness Scale scores scaled up along with the entropy and determinism parameters. Although other non-linear methods like Short Time Fourier Transform and wavelets are usually applied for analyzing the pupillary oscillations, they rely on strong assumptions like the stationarity of the signal or the a priori knowledge of the shape of the single basis wave. Instead, the cross-recurrence analysis of the non-linear dynamics of the pupil size oscillations is an adaptable diagnostic tool for identifying the different weight of the autonomic nervous system components in the modulation of pupil size changes at rest in non-luminance conditions.

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Time-frequency analysis of spontaneous pupillary oscillation signals using the Hilbert-Huang transform

Cited by 7 publications

References 26 publications

Sympathetic and Parasympathetic Modulation of Pupillary Unrest

Sympathetic and Parasympathetic Modulation of Pupillary Unrest

Empirical Mode Decomposition and Hilbert Spectrum for Abnormality Detection in Normal and Abnormal Walking Transitions

A Cross-Recurrence Analysis of the Pupil Size Fluctuations in Steady Scotopic Conditions

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