A Performance Comparison of Neural Network and SVM Classifiers Using EEG Spectral Features to Predict Epileptic Seizures

Epilepsy is one of the most common neurological disorders, and afflicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to significant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artificial neural network and sup port vector machine classifiers, using EEG spectral features to predict the onset of epileptic seizures. In addition, the study also examines the influence of EEG window size, feature selection, and data sampling on classification performance. A total of 216 generalised models were trained and tested on a public seizure database, which contained over 1300 hours of EEG data from 7 subjects. The results showed that ANN outperform SVM, when using spectral features (p = 0.035). The beta and gamma frequency bands were shown to be the best predictors of seizure onset. No significant differences in performance were determined for the dif ferent window sizes, or for the feature selection methods. The data sampling method significantly influenced the performance (p \u3c 0.001), and highlighted the importance of treating class imbalance in EEG datasets

Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms

This dissertation presents innovative approaches based on fuzzy logic in epileptic seizure detection and prediction from Electroencephalogram (EEG). The fuzzy rule-based algorithms were developed with the aim to improve quality of life of epilepsy patients by utilizing intelligent methods. An adaptive fuzzy logic system was developed to detect seizure onset in a patient specific way. Fuzzy if-then rules were developed to mimic the human reasoning and taking advantage of the combination in spatial-temporal domain. Fuzzy c-means clustering technique was utilized for optimizing the membership functions for varying patterns in the feature domain. In addition, application of the adaptive neuro-fuzzy inference system (ANFIS) is presented for efficient classification of several commonly arising artifacts from EEG. Finally, we present a neuro-fuzzy approach of seizure prediction by applying the ANFIS. Patient specific ANFIS classifier was constructed to forecast a seizure followed by postprocessing methods. Three nonlinear seizure predictive features were used to characterize changes prior to seizure. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. The ANFIS classifier was constructed based on these features as inputs. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. In this dissertation, the application of the neuro-fuzzy algorithms in epilepsy diagnosis and treatment was demonstrated by applying the methods on different datasets. Several performance measures such as detection delay, sensitivity and specificity were calculated and compared with results reported in literature. The proposed algorithms have potentials to be used in diagnostics and therapeutic applications as they can be implemented in an implantable medical device to detect a seizure, forecast a seizure, and initiate neurostimulation therapy for the purpose of seizure prevention or abortion

A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG

We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved

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

EEG-Based Classification and Advanced Warning of Epileptic Seizures

Epilepsy is the second most common neurological disease after stroke. Epileptics may suffer hundreds of seizures per day, yet one is enough to put a person in constant fear of the next. The sudden and unexpected onset of seizures has debilitating and sometimes fatal consequences. The development of a real-time seizure prediction and alerting device would greatly improve epileptics’ quality of life. Major challenges for such a device include determining predictive features and discovering the maximum prediction window. Using the novel approach of random forest classification on EEG data, this research investigates the predictive features among the common EEG frequency bands for one patient with partial complex and partial with secondarily generalized seizures. The impact on classifier performance of labeling the transitional brain states is also investigated, using a time-series accuracy graph. Predictive features are found as far as 40 minutes in advance of two seizures, specifically in the beta frequencies of one brain node. The random forest classifier does not perform well, but shows promise for improved performance with minor adjustments in training. The time-series accuracy graphs prove a useful tool for visualization and insight into classifier performance that is lacking in other evaluation methods

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

Musical components important for the Mozart K448 effect in epilepsy

There is growing evidence for the efficacy of music, specifically Mozart’s Sonata for Two Pianos in D Major (K448), at reducing ictal and interictal epileptiform activity. Nonetheless, little is known about the mechanism underlying this beneficial “Mozart K448 effect” for persons with epilepsy. Here, we measured the influence that K448 had on intracranial interictal epileptiform discharges (IEDs) in sixteen subjects undergoing intracranial monitoring for refractory focal epilepsy. We found reduced IEDs during the original version of K448 after at least 30-s of exposure. Nonsignificant IED rate reductions were witnessed in all brain regions apart from the bilateral frontal cortices, where we observed increased frontal theta power during transitions from prolonged musical segments. All other presented musical stimuli were associated with nonsignificant IED alterations. These results suggest that the “Mozart K448 effect” is dependent on the duration of exposure and may preferentially modulate activity in frontal emotional networks, providing insight into the mechanism underlying this response. Our findings encourage the continued evaluation of Mozart’s K448 as a noninvasive, non-pharmacological intervention for refractory epilepsy

Temporal Changes of Neocortical High-Frequency Oscillations in Epilepsy

High-frequency (100–500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100–250 Hz) or fast ripples (250–500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient\u27s entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation