1,110 research outputs found
Automatic Seizure Prediction using CNN and LSTM
The electroencephalogram (EEG) is one of the most precious technologies to
understand the happenings inside our brain and further understand our body's
happenings. Automatic prediction of oncoming seizures using the EEG signals
helps the doctors and clinical experts and reduces their workload. This paper
proposes an end-to-end deep learning algorithm to fully automate seizure
prediction's laborious task without any heavy pre-processing on the EEG data or
feature engineering. The proposed deep learning network is a blend of signal
processing and deep learning pipeline, which automates the seizure prediction
framework using the EEG signals. This proposed model was evaluated on an open
EEG dataset, CHB-MIT. The network achieved an average sensitivity of
97.746\text{\%} and a false positive rate (FPR) of 0.2373 per hour
Exploring machine learning techniques in epileptic seizure detection and prediction
Epilepsy is the most common neurological disorder, affecting between 0.6% and 0.8%
of the global population. Among those affected by epilepsy whose primary method of
seizure management is Anti Epileptic Drug therapy (AED), 30% go on to develop
resistance to drugs which ultimately leads to poor seizure management. Currently,
alternative therapeutic methods with successful outcome and wide applicability to
various types of epilepsy are limited. During an epileptic seizure, the onset of which
tends to be sudden and without prior warning, sufferers are highly vulnerable to injury,
and methods that might accurately predict seizure episodes in advance are clearly of
value, particularly to those who are resistant to other forms of therapy.
In this thesis, we draw from the body of work behind automatic seizure prediction
obtained from digitised Electroencephalography (EEG) data and use a selection of
machine learning and data mining algorithms and techniques in an attempt to explore
potential directions of improvement for automatic prediction of epileptic seizures. We
start by adopting a set of EEG features from previous work in the field (Costa et al.
2008) and exploring these via seizure classification and feature selection studies on a
large dataset. Guided by the results of these feature selection studies, we then build on
Costa et al's work by presenting an expanded feature-set for EEG studies in this area.
Next, we study the predictability of epileptic seizures several minutes (up to 25
minutes) in advance of the physiological onset. Furthermore, we look at the role of the
various feature compositions on predicting epileptic seizures well in advance of their
occurring. We focus on how predictability varies as a function of how far in advance
we are trying to predict the seizure episode and whether the predictive patterns are
translated across the entire dataset.
Finally, we study epileptic seizure detection from a multiple-patient perspective.
This entails conducting a comprehensive analysis of machine learning models trained
on multiple patients and then observing how generalisation is affected by the number of
patients and the underlying learning algorithm. Moreover, we improve multiple-patient
performance by applying two state of the art machine learning algorithms
Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy
Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy
Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions
Understanding the dynamics of spatially extended systems represents a
challenge in diverse scientific disciplines, ranging from physics and
mathematics to the earth and climate sciences or the neurosciences. This
challenge has stimulated the development of sophisticated data analysis
approaches adopting concepts from network theory: systems are considered to be
composed of subsystems (nodes) which interact with each other (represented by
edges). In many studies, such complex networks of interactions have been
derived from empirical time series for various spatially extended systems and
have been repeatedly reported to possess the same, possibly desirable,
properties (e.g. small-world characteristics and assortativity). In this thesis
we study whether and how interaction networks are influenced by the analysis
methodology, i.e. by the way how empirical data is acquired (the spatial and
temporal sampling of the dynamics) and how nodes and edges are derived from
multivariate time series. Our modeling and numerical studies are complemented
by field data analyses of brain activities that unfold on various spatial and
temporal scales. We demonstrate that indications of small-world characteristics
and assortativity can already be expected due solely to the analysis
methodology, irrespective of the actual interaction structure of the system. We
develop and discuss strategies to distinguish the properties of interaction
networks related to the dynamics from those spuriously induced by the analysis
methodology. We show how these strategies can help to avoid misinterpretations
when investigating the dynamics of spatially extended systems.Comment: PhD thesis, University of Bonn (Germany), published in 2012, 141
page
Epilepsy
Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy
- …