Seizure Prediction With HIVE-CODAs: The Hierarchical Vote Collective of Domain Adaptation Methods

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods are usually trained and tested on the same patient due to the interindividual variability. However, the challenging problem of the domain shift between different subjects remains unsolved, resulting in low prevalence of clinical application. In this study, a generic model based on the domain adaptation (DA) technique is proposed to alleviate such problems. Ensemble learning is employed by developing a hierarchical vote collective of seven DA modules over multi-modality data, such that the predictive performance is improved by training multiple models. Moreover, to increase the feasibility of its implementation, this study mimics the data distribution of clinical sampling and tests the model under this simulated realistic condition. Based on the performance of seven subnetworks, the applicability of each DA algorithm for seizure prediction is evaluated, which is the first study that provides the assessment. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can reduce the domain gap effectively compared with previous studies.

Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction

Epilepsy is a common neurological disease that affects more than 2 percent of the population globally. An imbalance in brain electrical activities causes unpredictable seizures, which eventually leads to epilepsy. Neurostimulators have the power to intervene in advance and avoid the occurrence of seizures. Its efficiency can be increased with the help of heuristics like advanced seizure prediction. Early identification of preictal state will help easy activation of neurostimulator on time. This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data. Through this study, we aim to find the best model, which can be used to create an ensemble model for better learning. This involves modeling and simulation of classical machine learning technique like Logistic regression, Naive Bayes model, K nearest neighbors Random Forest, and deep learning techniques like an Artificial neural network, Convolutional neural networks, Long short term memory, and Autoencoders. In this analysis, Random Forest and Long Short-Term Memory performed well among all models in terms of sensitivity and specificity.

Automatic Approach for Detecting the Seizure Using RCCN Architecture

Seizure is one of the most common neurodegenerative illnesses in humans, and it can result in serious brain damage, strokes, and tumors. Seizures can be detected early, which can assist prevent harm and aid in the treatment of epilepsy sufferers. A seizure prediction system's goal is to correctly detect the pre-ictal brain state, which occurs before a seizure occurs. Patient-independent seizure prediction models have been recognized as a real-world solution to the seizure prediction problem, since they are designed to provide accurate performance across different patients by using the recorded dataset. Furthermore, building such models to adjust to the significant inter-subject variability in EEG data has received little attention. We present a patient-independent deep learning architectures that can train a global function using data from numerous people with its own learning strategy. On the CHB- MIT-EEG dataset, the proposed models reach state-of-the-art accuracy for seizure prediction, with 95.54 percent accuracy. While predicting seizures, the Siamese model trained on the suggested learning technique is able to understand patterns associated to patient differences in data. Our models outperform the competition in terms of patient-independent seizure prediction, and following model adaption, the same architecture may be employed as a patient-specific classifier. We show that the MFCC feature map used by our models contains predictive biomarkers associated to inter-ictal and pre-ictal brain states, and we are the first study to use model interpretation to explain classifier behaviour for the task of seizure prediction.

Deep Learning Model for Epileptic Seizure Prediction

Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal and so this model can predict the seizure values only a few seconds earlier.

Power efficient refined seizure prediction algorithm based on an enhanced benchmarking

Deep learning techniques have led to significant advancements in seizure prediction research. However, corresponding used benchmarks are not uniform in published results. Moreover, inappropriate training and evaluation processes used in various work create overfitted models, making prediction performance fluctuate or unreliable. In this study, we analyzed the various data preparation methods, dataset partition methods in related works, and explained the corresponding impacts to the prediction algorithms. Then we applied a robust processing procedure that considers the appropriate sampling parameters and the leave-one-out cross-validation method to avoid possible overfitting and provide prerequisites for ease benchmarking. Moreover, a deep learning architecture takes advantage of a one-dimension convolutional neural network and a bi-directional long short-term memory network is proposed for seizure prediction. The architecture achieves 77.6% accuracy, 82.7% sensitivity, and 72.4% specificity, and it outperforms the indicators of other prior-art works. The proposed model is also hardware friendly; it has 6.274 k parameters and requires only 12.825 M floating-point operations, which is advantageous for memory and power constrained device implementations.

A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.