85 research outputs found
Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring
Use of inertial sensory systems to monitor and detect seizure episodes in patients suffering from epilepsy is investigated via numerical simulations and experiments. Numerical simulations employ a mathematical model that is able to predict human body dynamic responses during a typical epileptic seizure. An optimized inertial sensor placement procedure is developed to address achievement of highest possible sensing resolution in determining angular accelerations with minimal errors. In addition, a joint torque estimation procedure is formulated to assist in the future development of a possible detection scheme. Experimental motion data obtained from an epileptic seizure patient as well as a healthy subject via a cluster of inertial measurement sensors formed a basis for proposing a suitable detection scheme based on non-linear response analysis. In particular, preliminary experimental data analysis has shown that the proposed modified Poincaré Map based scheme can become an effective tool in detecting of seizure via inertial measurements
Seizure prediction : ready for a new era
Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin
Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution
Epileptic seizures are characterised by abnormal brain dynamics at multiple
scales, engaging single neurons, neuronal ensembles and coarse brain regions.
Key to understanding the cause of such emergent population dynamics, is
capturing the collective behaviour of neuronal activity at multiple brain
scales. In this thesis I make use of the larval zebrafish to capture single
cell neuronal activity across the whole brain during epileptic seizures.
Firstly, I make use of statistical physics methods to quantify the collective
behaviour of single neuron dynamics during epileptic seizures. Here, I
demonstrate a population mechanism through which single neuron dynamics
organise into seizures: brain dynamics deviate from a phase transition.
Secondly, I make use of single neuron network models to identify the synaptic
mechanisms that actually cause this shift to occur. Here, I show that the
density of neuronal connections in the network is key for driving generalised
seizure dynamics. Interestingly, such changes also disrupt network response
properties and flexible dynamics in brain networks, thus linking microscale
neuronal changes with emergent brain dysfunction during seizures. Thirdly, I
make use of non-linear causal inference methods to study the nature of the
underlying neuronal interactions that enable seizures to occur. Here I show
that seizures are driven by high synchrony but also by highly non-linear
interactions between neurons. Interestingly, these non-linear signatures are
filtered out at the macroscale, and therefore may represent a neuronal
signature that could be used for microscale interventional strategies. This
thesis demonstrates the utility of studying multi-scale dynamics in the larval
zebrafish, to link neuronal activity at the microscale with emergent properties
during seizures
Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme.The work
presented in this paper was supported by FP7 EU funded MICHELANGELO
project, Grant Agreement #288241
Dynamical Modeling Techniques for Biological Time Series Data
The present thesis is articulated over two main topics which have in common the modeling
of the dynamical properties of complex biological systems from large-scale time-series
data. On one hand, this thesis analyzes the inverse problem of reconstructing Gene Regulatory
Networks (GRN) from gene expression data. This first topic seeks to reverse-engineer
the transcriptional regulatory mechanisms involved in few biological systems of interest,
vital to understand the specificities of their different responses. In the light of recent mathematical
developments, a novel, flexible and interpretable modeling strategy is proposed
to reconstruct the dynamical dependencies between genes from short-time series data.
In addition, experimental trade-offs and optimal modeling strategies are investigated for
given data availability. Consistent literature on these topics was previously surprisingly
lacking. The proposed methodology is applied to the study of circadian rhythms, which
consists in complex GRN driving most of daily biological activity across many species.
On the other hand, this manuscript covers the characterization of dynamically differentiable
brain states in Zebrafish in the context of epilepsy and epileptogenesis. Zebrafish
larvae represent a valuable animal model for the study of epilepsy due to both their genetic
and dynamical resemblance with humans. The fundamental premise of this research
is the early apparition of subtle functional changes preceding the clinical symptoms of
seizures. More generally, this idea, based on bifurcation theory, can be described by a
progressive loss of resilience of the brain and ultimately, its transition from a healthy
state to another characterizing the disease. First, the morphological signatures of seizures
generated by distinct pathological mechanisms are investigated. For this purpose, a
range of mathematical biomarkers that characterizes relevant dynamical aspects of the
neurophysiological signals are considered. Such mathematical markers are later used to
address the subtle manifestations of early epileptogenic activity. Finally, the feasibility of
a probabilistic prediction model that indicates the susceptibility of seizure emergence over
time is investigated. The existence of alternative stable system states and their sudden
and dramatic changes have notably been observed in a wide range of complex systems
such as in ecosystems, climate or financial markets
Mean field modelling of human EEG: application to epilepsy
Aggregated electrical activity from brain regions recorded via an electroencephalogram (EEG),
reveal that the brain is never at rest, producing a spectrum of ongoing oscillations that
change as a result of different behavioural states and neurological conditions. In particular,
this thesis focusses on pathological oscillations associated with absence seizures that typically
affect 2–16 year old children. Investigation of the cellular and network mechanisms for absence
seizures studies have implicated an abnormality in the cortical and thalamic activity in the
generation of absence seizures, which have provided much insight to the potential cause of this
disease. A number of competing hypotheses have been suggested, however the precise cause
has yet to be determined. This work attempts to provide an explanation of these abnormal
rhythms by considering a physiologically based, macroscopic continuum mean-field model of
the brain's electrical activity. The methodology taken in this thesis is to assume that many
of the physiological details of the involved brain structures can be aggregated into continuum
state variables and parameters. The methodology has the advantage to indirectly encapsulate
into state variables and parameters, many known physiological mechanisms underlying the
genesis of epilepsy, which permits a reduction of the complexity of the problem. That is, a
macroscopic description of the involved brain structures involved in epilepsy is taken and then
by scanning the parameters of the model, identification of state changes in the system are
made possible. Thus, this work demonstrates how changes in brain state as determined in
EEG can be understood via dynamical state changes in the model providing an explanation
of absence seizures. Furthermore, key observations from both the model and EEG data
motivates a number of model reductions. These reductions provide approximate solutions of
seizure oscillations and a better understanding of periodic oscillations arising from the involved
brain regions. Local analysis of oscillations are performed by employing dynamical systems
theory which provide necessary and sufficient conditions for their appearance. Finally local
and global stability is then proved for the reduced model, for a reduced region in the parameter
space. The results obtained in this thesis can be extended and suggestions are provided for
future progress in this area
Epilepsy
With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well
Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud
Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with
minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data
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