329 research outputs found
Integration of Wavelet and Recurrence Quantification Analysis in Emotion Recognition of Bilinguals
Background: This study offers a robust framework for the classification of autonomic signals into five affective states during the picture viewing. To this end, the following emotion categories studied: five classes of the arousal-valence plane (5C), three classes of arousal (3A), and three categories of valence (3V). For the first time, the linguality information also incorporated into the recognition procedure. Precisely, the main objective of this paper was to present a fundamental approach for evaluating and classifying the emotions of monolingual and bilingual college students.Methods: Utilizing the nonlinear dynamics, the recurrence quantification measures of the wavelet coefficients extracted. To optimize the feature space, different feature selection approaches, including generalized discriminant analysis (GDA), principal component analysis (PCA), kernel PCA, and linear discriminant analysis (LDA), were examined. Finally, considering linguality information, the classification was performed using a probabilistic neural network (PNN).Results: Using LDA and the PNN, the highest recognition rates of 95.51%, 95.7%, and 95.98% were attained for the 5C, 3A, and 3V, respectively. Considering the linguality information, a further improvement of the classification rates accomplished.Conclusion: The proposed methodology can provide a valuable tool for discriminating affective states in practical applications within the area of human-computer interfaces
Design, implementation and realization of an integrated platform dedicated to e-public health, for analysing health data and supporting the management control in healthcare companies.
In healthcare, the information is a fundamental aspect and the human body is the major source of every kind of data: the challenge is to benefit from this huge amount of unstructured data by applying technologic solutions, called Big Data Analysis, that allows the management of data and the extraction of information through informatic systems. This thesis aims to introduce a technologic solution made up of two open source platforms: Power BI and Knime Analytics Platform. First, the importance, the role and the processes of business intelligence and machine learning in healthcare will be discussed; secondly, the platforms will be described, particularly enhancing their feasibility and capacities. Then, the clinical specialties, where they have been applied, will be shown by highlighting the international literature that have been produced: neurology, cardiology, oncology, fetal-monitoring and others. An application in the current pandemic situation due to SARS-CoV-2 will be described by using more than 50000 records: a cascade of 3 platforms helping health facilities to deal with the current worldwide pandemic.
Finally, the advantages, the disadvantages, the limitations and the future developments in this framework will be discussed while the architectural technologic solution containing a data warehouse, a platform to collect data, two platforms to analyse health and management data and the possible applications will be shown
Development of an EMG-based Muscle Health Model for Elbow Trauma Patients
Musculoskeletal (MSK) conditions are a leading cause of pain and disability worldwide. Rehabilitation is critical for recovery from these conditions and for the prevention of long-term disability. Robot-assisted therapy has been demonstrated to provide improvements to stroke rehabilitation in terms of efficiency and patient adherence. However, there are no wearable robot-assisted solutions for patients with MSK injuries. One of the limiting factors is the lack of appropriate models that allow the use of biosignals as an interface input. Furthermore, there are no models to discern the health of MSK patients as they progress through their therapy.
This thesis describes the design, data collection, analysis, and validation of a novel muscle health model for elbow trauma patients. Surface electromyography (sEMG) data sets were collected from the injured arms of elbow trauma patients performing 10 upper-limb motions. The data were assessed and compared to sEMG data collected from the patients\u27 contralateral healthy limbs. A statistical analysis was conducted to identify trends relating the sEMG signals to muscle health.
sEMG-based classification models for muscle health were developed. Relevant sEMG features were identified and combined into feature sets for the classification models. The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Classification models based on individual motions achieved cross-validation accuracies of 48.2--79.6%. Following feature selection and optimization of the models, cross-validation accuracies of up to 82.1% were achieved.
This work suggests that there is a potential for implementing an EMG-based model of muscle health in a rehabilitative elbow brace to assess patients recovering from MSK elbow trauma. However, more research is necessary to improve the accuracy and the specificity of the classification models
Probabilistic Graphical Models for ERP-Based Brain Computer Interfaces
An event related potential (ERP) is an electrical potential recorded from the nervous system of humans or other animals. An ERP is observed after the presentation of a stimulus. Some examples of the ERPs are P300, N400, among others. Although ERPs are used very often in neuroscience, its generation is not yet well understood and different theories have been proposed to explain the phenomena. ERPs could be generated due to changes in the alpha rhythm, an internal neural control that reset the ongoing oscillations in the brain, or separate and distinct additive neuronal phenomena. When different repetitions of the same stimuli are averaged, a coherence addition of the oscillations is obtained which explain the increase in amplitude in the signals. Two ERPs are mostly studied: N400 and P300. N400 signals arise when a subject tries to make semantic operations that support neural circuits for explicit memory. N400 potentials have been observed mostly in the rhinal cortex. P300 signals are related to attention and memory operations. When a new stimulus appears, a P300 ERP (named P3a) is generated in the frontal lobe. In contrast, when a subject perceives an expected stimulus, a P300 ERP (named P3b) is generated in the temporal – parietal areas. This implicates P3a and P3b are related, suggesting a circuit pathway between the frontal and temporal–parietal regions, whose existence has not been verified. Un potencial relacionado con un evento (ERP) es un potencial eléctrico registrado en el sistema nervioso de los seres humanos u otros animales. Un ERP se observa tras la presentación de un estÃmulo. Aunque los ERPs se utilizan muy a menudo en neurociencia, su generación aún no se entiende bien y se han propuesto diferentes teorÃas para explicar el fenómeno. Una interfaz cerebro-computador (BCI) es un sistema de comunicación en el que los mensajes o las órdenes que un sujeto envÃa al mundo exterior proceden de algunas señales cerebrales en lugar de los nervios y músculos periféricos. La BCI utiliza ritmos sensorimotores o señales ERP, por lo que se necesita un clasificador para distinguir entre los estÃmulos correctos y los incorrectos. En este trabajo, proponemos utilizar modelos probabilÃsticos gráficos para el modelado de la dinámica temporal y espacial de las señales cerebrales con aplicaciones a las BCIs. Los modelos gráficos han sido seleccionados por su flexibilidad y capacidad de incorporar información previa. Esta flexibilidad se ha utilizado anteriormente para modelar únicamente la dinámica temporal. Esperamos que el modelo refleje algunos aspectos del funcionamiento del cerebro relacionados con los ERPs, al incluir información espacial y temporal.DoctoradoDoctor en IngenierÃa Eléctrica y Electrónic
Image-guided port placement for minimally invasive cardiac surgery
Minimally invasive surgery is becoming popular for a number of interventions. Use of robotic surgical systems in coronary artery bypass intervention offers many benefits to patients, but is however limited by remaining challenges in port placement. Choosing the entry ports for the robotic tools has a large impact on the outcome of the surgery, and can be assisted by pre-operative planning and intra-operative guidance techniques. In this thesis, pre-operative 3D computed tomography (CT) imaging is used to plan minimally invasive robotic coronary artery bypass (MIRCAB) surgery. From a patient database, port placement optimization routines are implemented and validated. Computed port placement configurations approximated past expert chosen configurations with an error of 13.7 ±5.1 mm. Following optimization, statistical classification was used to assess patient candidacy for MIRCAB. Various pattern recognition techniques were used to predict MIRCAB success, and could be used in the future to reduce conversion rates to conventional open-chest surgery. Gaussian, Parzen window, and nearest neighbour classifiers all proved able to detect ‘candidate’ and ‘non-candidate’ MIRCAB patients. Intra-operative registration and laser projection of port placements was validated on a phantom and then evaluated in four patient cases. An image-guided laser projection system was developed to map port placement plans from pre-operative 3D images. Port placement mappings on the phantom setup were accurate with an error of 2.4 ± 0.4 mm. In the patient cases, projections remained within 1 cm of computed port positions. Misregistered port placement mappings in human trials were due mainly to the rigid-body registration assumption and can be improved by non-rigid techniques. Overall, this work presents an integrated approach for: 1) pre-operative port placement planning and classification of incoming MIRCAB patients; and 2) intra-operative guidance of port placement. Effective translation of these techniques to the clinic will enable MIRCAB as a more efficacious and accessible procedure
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
A Longitudinal Study on Boosting Students’ Performance with a Learning Companion
This study examines the impact of a coded virtual learning companion (LC) that interacts with students of an introductory information systems class throughout the semester. The LC is designed to motivate, advise on time management strategies, and study collaboratively. We conducted a between-subject longitudinal field experiment to investigate the LC’s impact on student motivation, time management, and learning outcomes. Statistical analysis, including a PLS-SEM model, shows that the LC significantly (p \u3c 0.05) improves extrinsic motivation, challenge, short-term planning, and time attitudes. A multiple mediator analysis confirms the role of motivation and time management as mediators between LC use and learning outcomes (subjective knowledge and exam scores). In addition, we conducted a qualitative workshop with the target group to identify barriers to LC adoption and derive mitigation strategies. Overall, our study reveals great potential to facilitate learning with LCs in higher education
Advancing the technology of sclera recognition
PhD ThesisEmerging biometric traits have been suggested recently to overcome
some challenges and issues related to utilising traditional human
biometric traits such as the face, iris, and fingerprint. In particu-
lar, iris recognition has achieved high accuracy rates under Near-
InfraRed (NIR) spectrum and it is employed in many applications for
security and identification purposes. However, as modern imaging
devices operate in the visible spectrum capturing colour images, iris
recognition has faced challenges when applied to coloured images
especially with eye images which have a dark pigmentation. Other
issues with iris recognition under NIR spectrum are the constraints on
the capturing process resulting in failure-to-enrol, and degradation in
system accuracy and performance. As a result, the research commu-
nity investigated using other traits to support the iris biometric in the
visible spectrum such as the sclera.
The sclera which is commonly known as the white part of the eye
includes a complex network of blood vessels and veins surrounding
the eye. The vascular pattern within the sclera has different formations
and layers providing powerful features for human identification. In
addition, these blood vessels can be acquired in the visible spectrum
and thus can be applied using ubiquitous camera-based devices. As a
consequence, recent research has focused on developing sclera recog-
nition. However, sclera recognition as any biometric system has issues
and challenges which need to be addressed. These issues are mainly
related to sclera segmentation, blood vessel enhancement, feature ex-
traction, template registration, matching and decision methods. In
addition, employing the sclera biometric in the wild where relaxed
imaging constraints are utilised has introduced more challenges such
as illumination variation, specular reflections, non-cooperative user
capturing, sclera blocked region due to glasses and eyelashes, variation
in capturing distance, multiple gaze directions, and eye rotation.
The aim of this thesis is to address such sclera biometric challenges
and highlight the potential of this trait. This also might inspire further
research on tackling sclera recognition system issues. To overcome the
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above-mentioned issues and challenges, three major contributions are
made which can be summarised as 1) designing an efficient sclera
recognition system under constrained imaging conditions which in-
clude new sclera segmentation, blood vessel enhancement, vascular
binary network mapping and feature extraction, and template registra-
tion techniques; 2) introducing a novel sclera recognition system under
relaxed imaging constraints which exploits novel sclera segmentation,
sclera template rotation alignment and distance scaling methods, and
complex sclera features; 3) presenting solutions to tackle issues related
to applying sclera recognition in a real-time application such as eye
localisation, eye corner and gaze detection, together with a novel image
quality metric.
The evaluation of the proposed contributions is achieved using five
databases having different properties representing various challenges
and issues. These databases are the UBIRIS.v1, UBIRIS.v2, UTIRIS,
MICHE, and an in-house database. The results in terms of segmen-
tation accuracy, Equal Error Rate (EER), and processing time show
significant improvement in the proposed systems compared to state-
of-the-art methods.Ministry of Higher Education and
Scientific Research in Iraq and the Iraqi Cultural Attach´e in Londo
Fuzzy Logic
The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy
Purpose: This study comprises two subprojects. In subproject one, the study
purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using
quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS)
in locally advanced breast cancer (LABC) during chemotherapy. In subproject
two, DOS-based functional maps were analysed with texture-based image
features to predict breast cancer response before the start of NAC.
Patients and Measurements: The institution’s ethics review board approved
this study. For subproject one, subjects (n=22) gave written consent before
participating in the study. Participants underwent non-invasive, DOS and QUS
imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before
surgical removal of the tumour (mastectomy and/or lumpectomy);
corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were
determined from tumour ultrasound data using spectral analysis. In the same
patients, DOS was used to measure parameters relating to tumour haemoglobin
and tissue composition such as %Water and %Lipids. Discriminant analysis
and receiver-operating characteristic (ROC) analyses were used to correlate the
measured imaging parameters to Miller-Payne pathological response during
treatment. Additionally, multivariate analysis was carried out for pairwise DOS
and QUS parameter combinations to determine if an increase in the
classification accuracy could be obtained using combination DOS and QUS
parametric models.
For subproject two, 15 additional patients we recruited after first giving
their written informed consent. A pooled analysis was completed for all DOS
baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients
planned for NAC had functional DOS maps and associated textural features
generated. A grey-level co-occurrence matrix (texture) analysis was completed
for parameters associated with haemoglobin, tissue composition, and optical
properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total
haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering
amplitude [SA]) prior to treatment. Textural features included contrast (con),
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correlation (cor), energy (ene), and homogeneity (hom). Patients were
classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological
response criteria after treatment completion. In order to test if baseline
univariate texture features could predict treatment response, a receiver
operating characteristic (ROC) analysis was performed, and the optimal
sensitivity, specificity and area under the curve (AUC) was calculated using
Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to
test 40 DOS-texture features and all possible bivariate combinations using a
naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were
included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and
validation and ROC analysis were performed to find the maximum sensitivity
and specificity of bivariate DOS-texture features.
Results: For subproject one, individual DOS and QUS parameters, including
the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin
(HbT) were significant markers for response outcome after one week of
treatment (p<0.01). Multivariate (pairwise) combinations increased the
sensitivity, specificity and AUC at this time; the SI+HbO2 showed a
sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment.
For subproject two, the results indicated that textural characteristics of
pre-treatment DOS parametric maps can differentiate treatment response
outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst
univariate parameters in predicting response to chemotherapy: sensitivity (%Sn)
and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of
87.8%. The highest predictors using multivariate (binary) combination features
were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0,
a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model.
Conclusion: DOS and QUS demonstrated potential as coincident markers for
treatment response and may potentially facilitate response-guided therapies.
Also, the results of this study demonstrated that DOS-texture analysis can be
used to predict breast cancer response groups prior to starting NAC using
baseline DOS measurements
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