Potentiation of the early visual response to learned danger signals in adults and adolescents

The reinforcing effects of aversive outcomes on avoidance behaviour are well established. However, their influence on perceptual processes is less well explored, especially during the transition from adolescence to adulthood. Using electroencephalography, we examined whether learning to actively or passively avoid harm can modulate early visual responses in adolescents and adults. The task included two avoidance conditions, active and passive, where two different warning stimuli predicted the imminent, but avoidable, presentation of an aversive tone. To avoid the aversive outcome, participants had to learn to emit an action (active avoidance) for one of the warning stimuli and omit an action for the other (passive avoidance). Both adults and adolescents performed the task with a high degree of accuracy. For both adolescents and adults, increased N170 event-related potential amplitudes were found for both the active and the passive warning stimuli compared with control conditions. Moreover, the potentiation of the N170 to the warning stimuli was stable and long lasting. Developmental differences were also observed; adolescents showed greater potentiation of the N170 component to danger signals. These findings demonstrate, for the first time, that learned danger signals in an instrumental avoidance task can influence early visual sensory processes in both adults and adolescents.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Cone signals and activity in myopia and emmetropia

Myopia (short-sightedness) is a common ocular disorder of children and young adults. Studies primarily using animal models have shown that the retina controls eye growth and the outer retina is likely to have a key role. One theory is that the proportion of L (long-wavelength-sensitive) and M (medium-wavelength-sensitive) cones is related to myopia development; with a high L/M cone ratio predisposing individuals to myopia. However, not all dichromats (persons with red-green colour vision deficiency) with extreme L/M cone ratios have high refractive errors. We predict that the L/M cone ratio will vary in individuals with normal trichromatic colour vision but not show a systematic difference simply due to refractive error. The aim of this study was to determine if L/M cone ratios in the central 30° are different between myopic and emmetropic young, colour normal adults. Information about L/M cone ratios was determined using the multifocal visual evoked potential (mfVEP). The mfVEP can be used to measure the response of visual cortex to different visual stimuli. The visual stimuli were generated and measurements performed using the Visual Evoked Response Imaging System (VERIS 5.1). The mfVEP was measured when the L and M cone systems were separately stimulated using the method of silent substitution. The method of silent substitution alters the output of three primary lights, each with physically different spectral distributions to control the excitation of one or more photoreceptor classes without changing the excitation of the unmodulated photoreceptor classes. The stimulus was a dartboard array subtending 30° horizontally and 30° vertically on a calibrated LCD screen. The m-sequence of the stimulus was 215-1. The N1-P1 amplitude ratio of the mfVEP was used to estimate the L/M cone ratio. Data were collected for 30 young adults (22 to 33 years of age), consisting of 10 emmetropes (+0.3±0.4 D) and 20 myopes (–3.4±1.7 D). The stimulus and analysis techniques were confirmed using responses of two dichromats. For the entire participant group, the estimated central L/M cone ratios ranged from 0.56 to 1.80 in the central 3°-13° diameter ring and from 0.94 to 1.91 in the more peripheral 13°-30° diameter ring. Within 3°-13°, the mean L/M cone ratio of the emmetropic group was 1.20±0.33 and the mean was similar, 1.20±0.26, for the myopic group. For the 13°-30° ring, the mean L/M cone ratio of the emmetropic group was 1.48±0.27 and it was slightly lower in the myopic group, 1.30±0.27. Independent-samples t-test indicated no significant difference between the L/M cone ratios of the emmetropic and myopic group for either the central 3°-13° ring (p=0.986) or the more peripheral 13°-30° ring (p=0.108). The similar distributions of estimated L/M cone ratios in the sample of emmetropes and myopes indicates that there is likely to be no association between the L/M cone ratio and refractive error in humans.

A method for estimating intrinsic noise in electroretinographic (ERG) signals

Purpose To develop a signal processing paradigm for extracting ERG responses to temporal sinusoidal modulation with contrasts ranging from below perceptual threshold to suprathreshold contrasts. To estimate the magnitude of intrinsic noise in ERG signals at different stimulus contrasts. Methods Photopic test stimuli were generated using a 4-primary Maxwellian view optical system. The 4-primary lights were sinusoidally temporally modulated in-phase (36 Hz; 2.5 - 50% Michelson). The stimuli were presented in 1 s epochs separated by a 1 ms blank interval and repeated 160 times (160.16 s duration) during the recording of the continuous flicker ERG from the right eye using DTL fiber electrodes. After artefact rejection, the ERG signal was extracted using Fourier methods in each of the 1 s epochs where a stimulus was presented. The signal processing allows for computation of the intrinsic noise distribution in addition to the signal to noise (SNR) ratio. Results We provide the initial report that the ERG intrinsic noise distribution is independent of stimulus contrast whereas SNR decreases linearly with decreasing contrast until the noise limit at ~2.5%. The 1ms blank intervals between epochs de-correlated the ERG signal at the line frequency (50 Hz) and thus increased the SNR of the averaged response. We confirm that response amplitude increases linearly with stimulus contrast. The phase response shows a shallow positive relationship with stimulus contrast. Conclusions This new technique will enable recording of intrinsic noise in ERG signals above and below perceptual visual threshold and is suitable for measurement of continuous rod and cone ERGs across a range of temporal frequencies, and post-receptoral processing in the primary retinogeniculate pathways at low stimulus contrasts. The intrinsic noise distribution may have application as a biomarker for detecting changes in disease progression or treatment efficacy.