3,434 research outputs found
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms
(EEGs) data can enable more precise diagnosis and efficient management of the
disease. This task is challenging due to factors such as low signal-to-noise
ratios, signal artefacts, high variance in seizure semiology among epileptic
patients, and limited availability of clinical data. To overcome these
challenges, in this paper, we present SeizureNet, a deep learning framework
which learns multi-spectral feature embeddings using an ensemble architecture
for cross-patient seizure type classification. We used the recently released
TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of
SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of
up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross
validation for scalp EEG based multi-class seizure type classification. We also
show that the high-level feature embeddings learnt by SeizureNet considerably
improve the accuracy of smaller networks through knowledge distillation for
applications with low-memory constraints
Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia
We apply flicker-noise spectroscopy (FNS), a time series analysis method
operating on structure functions and power spectrum estimates, to study the
clinical electroencephalogram (EEG) signals recorded in children/adolescents
(11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the
National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical
Sciences. The EEG signals for these subjects were compared with the signals for
a control sample of chronically depressed children/adolescents. The purpose of
the study is to look for diagnostic signs of subjects' susceptibility to
schizophrenia in the FNS parameters for specific electrodes and
cross-correlations between the signals simultaneously measured at different
points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes
at locations F3 and F4, which are symmetrically positioned in the left and
right frontal areas of cerebral cortex, respectively, demonstrates an essential
role of frequency-phase synchronization, a phenomenon representing specific
correlations between the characteristic frequencies and phases of excitations
in the brain. We introduce quantitative measures of frequency-phase
synchronization and systematize the values of FNS parameters for the EEG data.
The comparison of our results with the medical diagnoses for 84 subjects
performed at NCPH makes it possible to group the EEG signals into 4 categories
corresponding to different risk levels of subjects' susceptibility to
schizophrenia. We suggest that the introduced quantitative characteristics and
classification of cross-correlations may be used for the diagnosis of
schizophrenia at the early stages of its development.Comment: 36 pages, 6 figures, 2 tables; to be published in "Physica A
A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction
Quantitative analysis by renormalized entropy of invasive electroencephalograph recordings in focal epilepsy
Invasive electroencephalograph (EEG) recordings of ten patients suffering
from focal epilepsy were analyzed using the method of renormalized entropy.
Introduced as a complexity measure for the different regimes of a dynamical
system, the feature was tested here for its spatio-temporal behavior in
epileptic seizures. In all patients a decrease of renormalized entropy within
the ictal phase of seizure was found. Furthermore, the strength of this
decrease is monotonically related to the distance of the recording location to
the focus. The results suggest that the method of renormalized entropy is a
useful procedure for clinical applications like seizure detection and
localization of epileptic foci.Comment: 10 pages, 5 figure
Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data
High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures
Network perspectives on epilepsy using EEG/MEG source connectivity
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience
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