Building a patient-specific seizure detector without expert input using user triggered active learning strategies

Purpose: Patient-specific seizure detectors outperform general seizure detectors, but building them requires lots of consistently marked electroencephalogram (EEG) of a single patient, which is expensive to gather. This work presents a method to bring general seizure detectors up to par with patient-specific seizure detectors without expert input. The user/patient is only required to push a button in case of a false alarm and/or missed seizure. Method: For the experiments the 'CHB-MIT Scalp EEG Database' was used, which contains pre-surgically recorded EEG of 24 patients. The seizure detector used is based on (Buteneers et al. Epilepsy Research 2012:(in press)) combined with the preprocessing technique presented in (Shoeb et al. Epilepsy & Behavior 2004;5:483-598). Button presses mark the corresponding data and add it to the training set of the system. The performance is evaluated using leave-one-hour-out cross-validation to attain statistically relevant results. Results: For the patient-specific seizure detector 34(32)% (average(standard deviation)) of the detections are false, 8(14)% of the seizures are missed and a detection delay of 11(10)s is reached. The general seizure detector achieves: 86(89)%, 28(41)% and -35(82)s, respectively. Adding only false positives, the patient specific performance is achieved in 9 of the 24 patients. Adding missed seizures allows the patient-specific performance to be reached in 21 patients (about 90%). Conclusion: This work shows that in order to build a patient-specific seizure detector, no patient-specific EEG data is required for up to 90% of the patients using the presented technique

Switching characters between stimuli improves P300 speller accuracy

In this paper, an alternative stimulus presentation paradigm for the P300 speller is introduced. Similar to the checkerboard paradigm it minimizes the occurrence of the two most common causes of spelling errors: adjacency distraction and double flashes. Moreover, in contrast to the checkerboard paradigm, this new stimulus sequence does not increase the time required per stimulus iteration. Our new paradigm is compared to the basic row-column paradigm and the results indicate that, on average, the accuracy is improved

An uncued brain-computer interface using reservoir computing

Brain-Computer Interfaces are an important and promising avenue for possible next-generation assistive devices. In this article, we show how Reservoir Comput- ing – a computationally efficient way of training recurrent neural networks – com- bined with a novel feature selection algorithm based on Common Spatial Patterns can be used to drastically improve performance in an uncued motor imagery based Brain-Computer Interface (BCI). The objective of this BCI is to label each sample of EEG data as either motor imagery class 1 (e.g. left hand), motor imagery class 2 (e.g. right hand) or a rest state (i.e., no motor imagery). When comparing the re- sults of the proposed method with the results from the BCI Competition IV (where this dataset was introduced), it turns out that the proposed method outperforms the winner of the competition

A Bayesian model to estimate individual skull conductivity for EEG source imaging

EEG source imaging (ESI) techniques estimate 3D brain activity based on electrical activity measured on the scalp. In a clinical context, these techniques are typically used for the analysis of epileptiform activity. They play a central role in the pre-surgical planning prior to removal of the epileptic seizure focus, needed in about 30% of people with epilepsy [1]. ESI techniques make use of a parametric model of the geometry and electromagnetic properties of the subject’s head. While the geometry can be modelled precisely using an anatomical MR image of the head, there remains high uncertainty in the electrical conductivity of several types of tissue in the head (skull, white and gray matter, scalp etc.). Commonly, these conductivity values are set to a conventional value, based on previous studies. Because individual conductivity values can deviate radically from the conventional values (exceeding an order of magnitude) this can lead to errors that need to be avoided for accurate estimation of the epileptic focus location [2]. In this work, a first Bayesian model is proposed that is able to simultaneously estimate the source location and the subject specific skull conductivity from the measured EEG signals. The expectation-maximization algorithm was used to iteratively update the parameter estimation. As a first proof of concept, we used a three-layered spherical head model and a single dipole source to simulate electrical activity on the scalp, measured at 36 electrode positions, for a range of human skull conductivity values found in literature. We compared the source localization performance with our adaptive conductivity estimation to the performance with several conventional conductivity values used in previous studies. We found that, due to the high variation in individual skull conductivity values, the true source can be located more than 15mm away from the estimated source location using the conventional conductivity. Adaptive estimation of the conductivity with the Bayesian model lowers the maximum location error to only 3mm (see Figure 1). The first proof of concept looks promising and will be further deployed, including better probabilistic models for the variation in measured EEG, variation in dipole location and prior distribution of conductivity values. The final goal of this work is to estimate all tissue conductivity parameters, making the head model truly adaptive to the individual subject. [1] Strobbe G., Carrette E., Lopez J.D., Van Roost D., Meurs E., Vonck K., Boon P., Vandenberghe S., van Mierlo P. (2015) EEG source imaging of interictal spikes using multiple sparse volumetric priors for presurgical focus localization, NeuroImage, in preparation for submission. [2] Kassem A., Jackson D., Baumann S., Williams J., Wilton D., Fink P. and Prasky B. (1998) Effect of Conductivity Uncertainties and Modeling Errors on EEG Source Localization Using a 2-D Model, IEEE Transaction on Biomedical Engineering, vol. 45, no. 9, pp. 1135-114

Real-time epileptic seizure detection using reservoir computing

1 Purpose : This study proposes the use of a new classification algorithm, Reservoir Computing, to develop a real-time and accurate epileptic seizure detection system. 2 Methods: Reservoir Computing (RC) is a training method for recurrent neural networks where only a simple linear readout function is trained and where the neural network, the reservoir, is randomly created. As input for this reservoir we use a selection of different EEG features currently existing in seizure detection literature. This selection was made during training using a basic feature selection method. The output of the reservoir was trained using a ridge regression algorithm. 3 Results : In this study intracranial rat data from two different types of generalized epilepsy are detected: absence and tonic-clonic epilepsy. For both seizure types our approach resulted in an area under the Receiver Operating Characteristics curve (AUC) of 0.99 on the test data. For absences an average detection delay of 0.3s was noted, for tonic-clonic seizures this was 1.5s. The SWD detection method was tested on 15 hours of EEG-data coming from 13 GAERS rats, from which 10% was used for training. Our method outperformed the other implemented methods from which the best method was developed by Fanselow et al. in 2000 and resulted in an AUC of 0.96 and an average detection delay of more than 3 seconds. To evaluate the tonic-clonic seizure detection method 4 hours and 23 minutes of data of 4 rats was used. 20% of the total dataset was used for training, the rest was used for testing. Again our method outperformed other methods where the best method by White et al. in 2006 which resulted in a AUC of 0.82. 4 Conclusion : This study shows that it is possible to perform seizure detection using the described Reservoir Computing method and that it outperforms existing methods

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

A case study demonstrating the pitfalls during evaluation of a predictive seizure detector

Epilepsy is a neurological disorder characterized by recurring epileptic seizures that can occur at any given time. A system predicting these seizures could give a patient sufficient time to bring himself to safety and to apply a fast-working anti-epileptic treatment to suppress the upcoming seizure. Many seizure detection techniques claim to be able to detect seizures before the marked seizure onset on the EEG. In this work we study the predictions of such a seizure detection system. Materials: For the experiments the MIT Scalp EEG dataset was used, which contains at least 20 hours of EEG and 3 seizures for 24 pediatric patients [1]. Methods: The data is preprocessed using a filter-bank of 8 Butterworth filters of 3 Hz wide between 0.5 and 24.5 Hz [1]. Next the energy is determined for windows of 2 seconds wide with 1 second overlap. This data is presented as input for the machine learning component based on Reservoir Computing (RC) [1]. RC uses a randomly created recurrent artificial neural network, the reservoir, to map the input to a higher dimensional space. The system is trained using a linear readout of the reservoir. After this readout a simple thresholding technique is applied for classification [1]. Experiments and results: For each patient, the system is trained on the data of the 23 other patients. During training, the 2 minutes of EEG prior or following a seizure is not used. Next the system is evaluated on the data of the considered patient. Detections which occurred 10 minutes before the marked seizure onset were considered as true positives. This resulted in a system that was able to detect 75% of the seizures with about 6 false positives per correctly detected seizure. For 11 out of 24 patients some seizures were detected before the marked seizure onset. Furthermore, in 4 of these patients at least half of the seizures were detected before the marked onset, and in a single patient all seizures were detected before the marked onset. Discussion: However, in retrospect, 65% of the early detections are caused by EEG artifacts. Most others can be attributed to inter-ictal spike and wave discharges in the EEG preceding the seizure. Only 3% of the early detections have currently an unknown cause and could be actual early detections. Although nearly all early detections can be considered as false positives. However such false positives have a significantly greater occurrence right before marked seizure onsets, but further research is needed to analyze the cause of this correlation. It might be that these artifacts contain predictive information or for example that the selection criteria for adding EEG sections to the dataset were less strict for EEG sections containing a seizure. These pitfalls call for common guidelines and datasets to evaluate early seizure detection methods. References: [1] Buteneers, P. (2012). Detection of epileptic seizures: the reservoir computing approach (Doctoral dissertation, Ghent University)