986 resultados para Pupil Diameter (PD)


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Objective To determine the effects of the administration of subconjunctival 1% atropine (SA), topical 1% atropine (A), 0.5% tropicamide (T), 1% homatropine (H), 10% phenylephrine (P), and 2% ibopamine (I) on intraocular pressure (IOP), pupil diameter (PD), ruminal motility (RM) and intestinal motility (IM) in sheep.Animal studied Ten spayed ewes of Santa Ines breed.Procedures Six experiments were performed separately at 1-week intervals. One eye was randomly selected and received one drop of A, T, H, P, I, or subconjunctival injection of atropine at 8 a. m. On the following days, IOP and PD were evaluated every 8 h until the pupil returned to its normal diameter. Ruminal motility and intestinal motility were evaluated only within the first 13 h.Results The IOP did not change significantly in the treated eyes compared with the control eyes and baseline at any time point (P > 0.05). A longer-lasting pupil dilation was observed after the administration of A (96 h), SA (79 h), H (24 h), and T (24 h). Within the first 30 min after treatment, RM and IM decreased, by 78% and 82% (H), 76% and 86% (SA), 46% and 58% (A), and 62% and 70% (T) (P < 0.001), respectively, with a tendency to return to baseline values following 13 h of drug administration. Both 10% phenylephrine and 2% ibopamine did not have any effect on the parameters evaluated (P > 0.05).Conclusions Topical and subconjunctival 1% atropine, 0.5% tropicamide, and 1% homatropine significantly reduced RM and IM, and induced pupil dilation but did not change IOP in eyes of healthy sheep. The sympathomimetics phenylephrine (10%) and ibopamine (2%) did not change the parameters evaluated.

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Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

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Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

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Purpose: We investigated the interaction between adapting field size and luminance on pupil diameter when cones alone (photopic) or rods and cones (mesopic) were active. Method: Circular achromatic targets (1o to 24o diameter) were presented to eight young participants on a rectangular projector screen. The accommodative influence on pupil diameter was minimized using cycloplegia in the fixing right eye and the consensual pupil reflex was measured in the left eye. Target luminance was adjusted for each stimulus such that corneal flux density (product of field area and luminance) was constant at 3600 cd.deg2m-2 (photopic condition) and 1.49 cd.deg2m-2 (mesopic condition). Results: There were no statistically significant effects of adaptive field size on pupil diameter for either condition. Conclusion: If corneal flux density is kept constant, there will be no change in pupil diameter as the size of the stimulus field increases at either mesopic or photopic lighting levels up to at least 24°.

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Purpose to evaluate the effects of the wearer’s pupil size and spherical aberration on visual performance with centre-near, aspheric multifocal contact lenses (MFCLs). The advantage of binocular over monocular vision was also investigated. Methods Twelve young volunteers, with an average age of 27±5 years, participated in the study. LogMAR Visual Acuity (VA) was measured under cycloplegia for a range of defocus levels (from +3.0 to -3.0D, in 0.5D steps) with no correction and with three aspheric MFCLs (Air Optix Aqua Multifocal, Ciba Vision, Duluth, GA, US) with a centre-near design, providing correction for “Low”, “Med” and “High” near demands. Measurements were performed for all combinations of the following conditions: i) artificial pupils of 6mm and 3mm diameter, ii) binocular and monocular (dominant eye) vision. Depth-of-focus (DOF) was calculated from the VA vs. defocus curves. Ocular aberrations under cycloplegia were measured using iTrace. Results VA at -3.0D defocus (simulating near performance) was statistically higher for the 3mm than for the 6mm pupil (p=0.006), and for binocular rather than for monocular vision (p<0.001). Similarly, DOF was better for the 3mm pupil (p=0.002) and for binocular viewing conditions (p<0.001, ANOVA). Both VA at –3.0D defocus and DOF increased as the “addition” of the MFCL correction increased. Finally, with the centre-near MFCLs a linear correlation was found between VA at –3.0D defocus and the wearer’s ocular spherical aberration (R2=0.20 p<0.001 for 6mm data), with the eyes exhibiting the higher positive spherical aberration experiencing lower VAs. By contrast, no correlation was found between VA and spherical aberration at 0.00D defocus (distance vision). Conclusions Both near VA and depth-of-focus improve with these MFCLs, with the effects being more pronounced for small pupils and binocular than for monocular vision. Coupling of the wearer’s ocular spherical aberration with the aberration profiles provided by MFCLs affects their functionality.

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Background - The aim was to derive equations for the relationship between unaided vision and age, pupil diameter, iris colour and sphero-cylindrical refractive error. Methods - Data were collected from 663 healthy right eyes of white subjects aged 20 to 70 years. Subjective sphero-cylindrical refractive errors ranged from -6.8 to +9.4 D (mean spherical equivalent), -1.5 to +1.9 D (orthogonal component, J0) and -0.8 to 1.0 D (oblique component, J45). Cylinder axis orientation was orthogonal in 46 per cent of the eyes and oblique in 18 per cent. Unaided vision (-0.3 to +1.3 logMAR), pupil diameter (2.3 to 7.5 mm) and iris colour (67 per cent light/blue irides) was recorded. The sample included mostly females (60 per cent) and many contact lens wearers (42 per cent) and so the influences of these parameters were also investigated. Results - Decision tree analysis showed that sex, iris colour, contact lens wear and cylinder axis orientation did not influence the relationship between unaided vision and refractive error. New equations for the dependence of the minimum angle of resolution on age and pupil diameter arose from step backwards multiple linear regressions carried out separately on the myopes (2.91.scalar vector +0.51.pupil diameter -3.14 ) and hyperopes (1.55.scalar vector + 0.06.age – 3.45 ). Conclusion - The new equations may be useful in simulators designed for teaching purposes as they accounted for 81 per cent (for myopes) and 53 per cent (for hyperopes) of the variance in measured data. In comparison, previously published equations accounted for not more than 76 per cent (for myopes) and 24 per cent (for hyperopes) of the variance depending on whether they included pupil size. The new equations are, as far as is known to the authors, the first to include age. The age-related decline in accommodation is reflected in the equation for hyperopes.

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Purpose: To investigate the relationship between pupil diameter and refractive error and how refractive correction, target luminance, and accommodation modulate this relationship. Methods: Sixty emmetropic, myopic, and hyperopic subjects (age range, 18 to 35 years) viewed an illuminated target (luminance: 10, 100, 200, 400, 1000, 2000, and 4100 cd/m2) within a Badal optical system, at 0 diopters (D) and −3 D vergence, with and without refractive correction. Refractive error was corrected using daily disposable contact lenses. Pupil diameter and accommodation were recorded continuously using a commercially available photorefractor. Results: No significant difference in pupil diameter was found between the refractive groups at 0 D or −3 D target vergence, in the corrected or uncorrected conditions. As expected, pupil diameter decreased with increasing luminance. Target vergence had no significant influence on pupil diameter. In the corrected condition, at 0 D target vergence, the accommodation response was similar in all refractive groups. At −3 D target vergence, the emmetropic and myopic groups accommodated significantly more than the hyperopic group at all luminance levels. There was no correlation between accommodation response and pupil diameter or refractive error in any refractive group. In the uncorrected condition, the accommodation response was significantly greater in the hyperopic group than in the myopic group at all luminance levels, particularly for near viewing. In the hyperopic group, the accommodation response was significantly correlated with refractive error but not pupil diameter. In the myopic group, accommodation response level was not correlated with refractive error or pupil diameter. Conclusions: Refractive error has no influence on pupil diameter, irrespective of refractive correction or accommodative demand. This suggests that the pupil is controlled by the pupillary light reflex and is not driven by retinal blur.

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Purpose: to determine whether pupil dilation affects biometric measurements and intraocular lens (IOL) power calculation made using the new swept-source optical coherence tomography-based optical biometer (IOLMaster 700©; Carl Zeiss Meditec, Jena, Germany). Procedures: eighty-one eyes of 81 patients evaluated for cataract surgery were prospectively examined using the IOLMaster 700© before and after pupil dilation with tropicamide 1%. The measurements made were: axial length (AL), central corneal thickness (CCT), aqueous chamber depth (ACD), lens thickness (LT), mean keratometry (MK), white-to-white distance (WTW) and pupil diameter (PD). Holladay II and SRK/T formulas were used to calculate IOL power. Agreement between measurement modes (with and without dilation) was assessed through intraclass correlation coefficients (ICC) and Bland-Altman plots. Results: mean patient age was 75.17 ± 7.54 years (range: 57–92). Of the variables determined, CCT, ACD, LT and WTW varied significantly according to pupil dilation. Excellent intraobserver correlation was observed between measurements made before and after pupil dilation. Mean IOL power calculation using the Holladay 2 and SRK/T formulas were unmodified by pupil dilation. Conclusions: the use of pupil dilation produces statistical yet not clinically significant differences in some IOLMaster 700© measurements. However, it does not affect mean IOL power calculation.

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Pós-graduação em Cirurgia Veterinária - FCAV

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The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

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Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

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Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

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Purpose To compare measurements taken using a swept-source optical coherence tomography-based optical biometer (IOLmaster 700) and an optical low-coherence reflectometry biometer (Lenstar 900), and to determine the clinical impacts of differences in their measurements on intraocular lens (IOL) power predictions. Methods Eighty eyes of 80 patients scheduled to undergo cataract surgery were examined with both biometers. The measurements made using each device were axial length (AL), central corneal thickness (CCT), aqueous depth (AQD), lens thickness (LT), mean keratometry (MK), white-to-white distance (WTW), and pupil diameter (PD). Holladay 2 and SRK/T formulas were used to calculate IOL power. Differences in measurement between the two biometers were determined using the paired t-test. Agreement was assessed through intraclass correlation coefficients (ICC) and Bland–Altman plots. Results Mean patient age was 76.3±6.8 years (range 59–89). Using the Lenstar, AL and PD could not be measured in 12.5 and 5.25% of eyes, respectively, while IOLMaster 700 took all measurements in all eyes. The variables CCT, AQD, LT, and MK varied significantly between the two biometers. According to ICCs, correlation between measurements made with both devices was excellent except for WTW and PD. Using the SRK/T formula, IOL power prediction based on the data from the two devices were statistically different, but differences were not clinically significant. Conclusions No clinically relevant differences were detected between the biometers in terms of their measurements and IOL power predictions. Using the IOLMaster 700, it was easier to obtain biometric measurements in eyes with less transparent ocular media or longer AL.

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Purpose: To determine the relative contributions of rods, cones and melanopsin to pupil responses in humans using temporal sinusoidal stimulation for light levels spanning the low mesopic to photopic range. Methods: A four-primary Ganzfeld photostimulator controlled flicker stimulations at seven light levels (-2.7 to 2 log cd/m2) and five frequencies (0.5 to 8Hz). Pupil diameter was measured using a high-resolution eyetracker. Three kinds of sinusoidal photoreceptor modulations were generated using silent substitution: 1) rod modulation, 2) cone modulation, and 3) combined rod and cone modulation in phase (Experiment 1) or phase shifted (Experiment 2) from a fixed rod phase. The melanopsin excitation was computed for each condition. A vector sum model was used to estimate the relative contribution of rods, cones and melanopsin to the pupil response. Results: From Experiment 1, the pupil frequency response peaked at 1Hz at two mesopic light levels for the three modulation conditions. Analyzing the rod-cone phase difference for the combined modulations (Experiment 2) identified a V-shaped response amplitude with a minimum between 135° and 180°. The pupil response phases increased as cone modulation phase increased. The pupil amplitude increased with increasing light level for cone and combined in-phase rod and cone modulation, but not for the rod modulation. Conclusions: These results demonstrate that cone- and rod-pathway contributions are more predominant than melanopsin contribution to the phasic pupil response. The combined rod, cone and melanopsin inputs to the phasic state of the pupil light reflex follow linear summation.

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Rods, cones and melanopsin containing intrinsically photosensitive retinal ganglion cells (ipRGCs) operate in concert to regulate pupil diameter. The temporal properties of intrinsic ipRGC signalling are distinct to those of rods and cones, including longer latencies and sustained signalling after light offset. We examined whether the melanopsin mediated post-illumination pupil response (PIPR) and pupil constriction were dependent upon the inter-stimulus interval (ISI) between successive light pulses and the temporal frequency of sinusoidal light stimuli. Melanopsin excitation was altered by variation of stimulus wavelength (464 nm and 638 nm lights) and irradiance (11.4 and 15.2 log photons cm(-2) s(-1)). We found that 6s PIPR amplitude was independent of ISI and temporal frequency for all melanopsin excitation levels, indicating complete summation. In contrast to the PIPR, the maximum pupil constriction increased with increasing ISI with high and low melanopsin excitation, but time to minimum diameter was slower with high melanopsin excitation only. This melanopsin response to briefly presented pulses (16 and 100 ms) slows the temporal response of the maximum pupil constriction. We also demonstrate that high melanopsin excitation attenuates the phasic peak-trough pupil amplitude compared to conditions with low melanopsin excitation, indicating an interaction between inner and outer retinal inputs to the pupil light reflex. We infer that outer retina summation is important for rapidly controlling pupil diameter in response to short timescale fluctuations in illumination and may occur at two potential sites, one that is presynaptic to extrinsic photoreceptor input to ipRGCs, or another within the pupil control pathway if ipRGCs have differential temporal tuning to extrinsic and intrinsic signalling.