26 research outputs found

    Developing artificial intelligence models for classification of brain disorder diseases based on statistical techniques

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    The Abstract is currently unavailable, due to the thesis being under Embargo

    Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach

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    Crohn's disease (CD) diagnosis is a tremendouslyserious health problem due to its ultimately effecton the gastrointestinal tract that leads to the need of complexmedical assistance. In this study, the backpropagationneural network fuzzy classifier and a neuro-fuzzy modelare combined for diagnosing the CD. Factor analysis isused for data dimension reduction. The effect on the systemperformance has been investigated when using fuzzypartitioning and dimension reduction. Additionally, furthercomparison is done between the different levels of thefuzzy partition to reach the optimal performance accuracylevel. The performance evaluation of the proposed systemis estimated using the classification accuracy and othermetrics. The experimental results revealed that the classificationwith level-8 partitioning provides a classificationaccuracy of 97.67 %, with a sensitivity and specificity of96.07 and 100 %, respectively

    Intelligent Biosignal Analysis Methods

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    This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

    Coherence analysis : methods, solutions and problems

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    A coherence function is a measure of the correlation of two signals and may be used as a measure for functional relationship between brain areas. In studying functional relationships, referenced EEG (REEG) coherence analysis yields important new aspects of brain activities, which complement the data obtained by power spectral analysis. However, REEG-based coherence tends to show a false high value due to volume conduction from un correlated sources (VCUS). Existing signal processing methods address this issue using a Fourier coherence function of scalp Laplacian. Although this method has been proved useful to reveal correlation between EEG signals with minimum VCUS effects, it only provides frequency-domain analysis. Since EEG signals are highly non-stationary, it is more appropriate to use time-frequency methods for coherence analysis of scalp Laplacian. Thus this research applies the wavelet transform on coherence analysis of scalp Laplacian. To verify our technique, already recorded EEG data of event related potentials were obtained from a study of two large groups of alcoholic and abstinent alcoholic subjects, performing visual picture-recognition tasks. The proposed coherence method successfully detected time-frequency correlation between EEG signals with minimum VCUS effects. It showed significant spatial specificity and revealed detailed coherence patterns. Some new important results regarding time-frequency characteristics of VCUS effects on wavelet and short-time Fourier transform (STFT) coherence analysis of REEG signals were deduced. The proposed coherence method was also compared to a conventional wavelet coherence method of REEG signals in the study of coherence difference between coherences of alcoholic and abstinent alcoholic EEG signals. Results of this study provided substantial evidence that VCUS effects are not additive and therefore can not be ignored in comparison of different brain states between groups of subjects.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Advanced Signal Processing in Wearable Sensors for Health Monitoring

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    Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

    Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

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    This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

    Addictions

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    Addiction, increasingly perceived as a heterogeneous brain disorder, is one of the most peculiar psychiatric pathologies in that its management involves various, often non-overlapping, resources from the biological, psychological, medical, economical, social, and legal realms. Despite extensive efforts from the players of these various fields, to date there are no reliably effective treatments of addiction. This may stem from a lack of understanding of the etiology and pathophysiology of this disorder as well as from the lack of interest into the potential differences among patients in the way they interact compulsively with their drug. This book offers an overview of the psychobiology of addiction and its current management strategies from pharmacological, social, behavioural, and psychiatric points of view
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