A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

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.

Signal processing techniques for reducing the impact of fiber nonlinearities on system performance

Signal processing techniques for mitigating intra-channel and inter-channel fiber nonlinearities are reviewed. More detailed descriptions of three specific examples highlight the diversity of the electronic and optical approaches that have been investigated.

Two-Path All-pass Based Half-Band Infinite Impulse Response Decimation Filters and the Effects of Their Non-Linear Phase Response on ECG Signal Acquisition

This paper is based on the novel use of a very high fidelity decimation filter chain for Electrocardiogram (ECG) signal acquisition and data conversion. The multiplier-free and multi-stage structure of the proposed filters lower the power dissipation while minimizing the circuit area which are crucial design constraints to the wireless noninvasive wearable health monitoring products due to the scarce operational resources in their electronic implementation. The decimation ratio of the presented filter is 128, working in tandem with a 1-bit 3rd order Sigma Delta (ΣΔ) modulator which achieves 0.04 dB passband ripples and -74 dB stopband attenuation. The work reported here investigates the non-linear phase effects of the proposed decimation filters on the ECG signal by carrying out a comparative study after phase correction. It concludes that the enhanced phase linearity is not crucial for ECG acquisition and data conversion applications since the signal distortion of the acquired signal, due to phase non-linearity, is insignificant for both original and phase compensated filters. To the best of the authors’ knowledge, being free of signal distortion is essential as this might lead to misdiagnosis as stated in the state of the art. This article demonstrates that with their minimal power consumption and minimal signal distortion features, the proposed decimation filters can effectively be employed in biosignal data processing units.

The role of cardiopulmonary signals in the dynamics of the eye's wavefront aberrations

Abstract—The role of cardiopulmonary signals in the dynamics of wavefront aberrations in the eye has been examined. Synchronous measurement of the eye’s wavefront aberrations, cardiac function, blood pulse, and respiration signals were taken for a group of young, healthy subjects. Two focusing stimuli, three breathing patterns, as well as natural and cycloplegic eye conditions were examined. A set of tools, including time–frequency coherence and its metrics, has been proposed to acquire a detailed picture of the interactions of the cardiopulmonary system with the eye’s wavefront aberrations. The results showed that the coherence of the blood pulse and its harmonics with the eye’s aberrations was, on average, weak (0.4 ± 0.15), while the coherence of the respiration signal with eye’s aberrations was, on average, moderate (0.53 ± 0.14). It was also revealed that there were significant intervals during which high coherence occurred. On average, the coherence was high (>0.75) during 16% of the recorded time, for the blood pulse, and 34% of the time for the respiration signal. A statistically significant decrease in average coherence was noted for the eye’s aberrations with respiration in the case of fast controlled breathing (0.5 Hz). The coherence between the blood pulse and the defocus was significantly larger for the far target than for the near target condition. After cycloplegia, the coherence of defocus with the blood pulse significantly decreased, while this was not the case for the other aberrations. There was also a noticeable, but not statistically significant, increase in the coherence of the comatic term and respiration in that case. By using nonstationary measures of signal coherence, a more detailed picture of interactions between the cardiopulmonary signals and eye’s wavefront aberrations has emerged.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Study of phase synchronization in multichannel seizure EEG using nonlinear recurrence measure

Complex biological systems such as the human brain can be expected to be inherently nonlinear and hence difficult to model. Most of the previous studies on investigations of brain function have either used linear models or parametric nonlinear models. In this paper, we propose a novel application of a nonlinear measure of phase synchronization based on recurrences, correlation between probabilities of recurrence (CPR), to study seizures in the brain. The advantage of this nonparametric method is that it makes very few assumptions thus making it possible to investigate brain functioning in a data-driven way. We have demonstrated the utility of CPR measure for the study of phase synchronization in multichannel seizure EEG recorded from patients with global as well as focal epilepsy. For the case of global epilepsy, brain synchronization using thresholded CPR matrix of multichannel EEG signals showed clear differences in results obtained for epileptic seizure and pre-seizure. Brain headmaps obtained for seizure and preseizure cases provide meaningful insights about synchronization in the brain in those states. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Comparative studies with linear correlation have shown that the nonlinear measure CPR outperforms the linear correlation measure. (C) 2014 Elsevier Ltd. All rights reserved.

Multimodal therapy for complete regression of malignant melanoma using constrained nonlinear optimal dynamic inversion

Using a realistic nonlinear mathematical model for melanoma dynamics and the technique of optimal dynamic inversion (exact feedback linearization with static optimization), a multimodal automatic drug dosage strategy is proposed in this paper for complete regression of melanoma cancer in humans. The proposed strategy computes different drug dosages and gives a nonlinear state feedback solution for driving the number of cancer cells to zero. However, it is observed that when tumor is regressed to certain value, then there is no need of external drug dosages as immune system and other therapeutic states are able to regress tumor at a sufficiently fast rate which is more than exponential rate. As model has three different drug dosages, after applying dynamic inversion philosophy, drug dosages can be selected in optimized manner without crossing their toxicity limits. The combination of drug dosages is decided by appropriately selecting the control design parameter values based on physical constraints. The process is automated for all possible combinations of the chemotherapy and immunotherapy drug dosages with preferential emphasis of having maximum possible variety of drug inputs at any given point of time. Simulation study with a standard patient model shows that tumor cells are regressed from 2 x 107 to order of 105 cells because of external drug dosages in 36.93 days. After this no external drug dosages are required as immune system and other therapeutic states are able to regress tumor at greater than exponential rate and hence, tumor goes to zero (less than 0.01) in 48.77 days and healthy immune system of the patient is restored. Study with different chemotherapy drug resistance value is also carried out. (C) 2014 Elsevier Ltd. All rights reserved.

Herramienta gráfica para el desarrollo de prácticas de laboratorio basadas en el procesado digital de señal electrocardiográfica.

[ES]Este Trabajo Fin de Grado consiste en el diseño de una herramienta gráfica para el desarrollo de prácticas de laboratorio basadas en el procesado de señales electrocardiográficas (ECG) para la asignatura de Procesado de Señales Biomédicas. El proyecto se concreta en la aplicación de la herramienta para tres prácticas de laboratorio diferentes basadas en ECG, las cuales tendrán objetivos y técnicas de procesado diferentes. El objetivo final es que los docentes y los estudiantes sean capaces de trabajar con señales de ECG, analizar sus principales características y comprender distintas técnicas y algoritmos de procesado típicas de este ámbito.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Methodology for Validating Artifact Removal Techniques for Physiological Signals

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a ground truth signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this ground truth, together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform. © 2012 IEEE.