fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction

時間周波数領域でのてんかん脳波識別に関する研究 ‐平均二乗根に基づく特徴抽出に着目して‐

Epilepsy affects over 50 million people on an average yearly world wide. Epileptic Seizure is a generalised term which has broad classification depending on the reasons behind its occurrence. Parvez et al. when applied feature instantaneous bandwidth B2AM and time averaged bandwidth B2FM for classification of interictal and ictal on Freiburg data base, the result dipped low to 77.90% for frontal lobe whereas it was 80.20% for temporal lobe compare to the 98.50% of classification accuracy achieved on Bonn dataset with same feature for classification of ictal against interictal. We found reasons behind such low results are, first Parvez et al. has used first IMF of EMD for feature computation which mostly noised induce. Secondly, they used same kernel parameters of SVM as Bajaj et al. which they must have optimised with different dataset. But the most important reason we found is that two signals s1 and s2 can have same instantaneous bandwidth. Therefore, the motivation of the dissertation is to address the drawback of feature instantaneous bandwidth by new feature with objective of achieving comparable classification accuracy. In this work, we have classified ictal from healthy nonseizure interictal successfully first by using RMS frequency and another feature from Hilbert marginal spectrum then with its parameters ratio. RMS frequency is the square root of sum of square bandwidth and square of center frequency. Its contributing parameters ratio is ratio of center frequency square to square bandwidth. We have also used dominant frequency and its parameters ratio for the same purpose. Dominant frequency have same physical relevance as RMS frequency but different by definition, i.e. square root of sum of square of instantaneous band- width and square of instantaneous frequency. Third feature that we have used is by exploiting the equivalence of RMS frequency and dominant frequency (DF) to define root mean instantaneous frequency square (RMIFS) as square root of sum of time averaged bandwidth square and center frequency square. These features are average measures which shows good discrimination power in classifying ictal from interictal using SVM. These features, fr and fd also have an advantage of overcoming the draw back of square bandwidth and instantaneous bandwidth. RMS frequency that we have used in this work is different from generic root mean square analysis. We have used an adaptive thresholding algorithm to address the issue of false positive. It was able to increase the specificity by average of 5.9% on average consequently increasing the accuracy. Then we have applied morphological component analysis (MCA) with the fractional contribution of dominant frequency and other rest of the features like band- width parameter’s contribution and RMIFS frequency and its parameters and their ratio. With the results from proposed features, we validated our claim to overcome the drawback of instantaneous bandwidth and square bandwidth.九州工業大学博士学位論文 学位記番号：生工博甲第323号 学位授与年月日：平成30年6月28日1 Introduction|2 Empirical Mode Decomposition|3 Root Mean Square Frequency|4 Root Mean Instantaneous Frequency Square|5 Morphological Component Analysis|6 Conclusion九州工業大学平成30年

Classification of EEG Signals for Prediction of Epileptic Seizures

Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8% , and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.publishedVersio

Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. The proposed method is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, the proposed method does not require additional preprocessing and extraction of features which is common in the existing methods. The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, the proposed method outperformed them in terms of sensitivity, specificity and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity.The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR = 1dB, 84% when SNR = 5dB, and 88% when SNR = 10dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.NPRP 7-684-1-127, from the Qatar National Research Fund, a member of Qatar Foundation

Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features

Diagnosis of epilepsy or epileptic transients AEP (Abnormal Epileptiform Paroxysmal) is tedious, but important, and an expensive process. The process involves trained neurologists going over the patient\u27s EEG records looking for epileptiform discharge like events and classifying it as AEP (Abnormal Epileptiform Paroxysmal) or non-AEP. The objective of this research is to automate the process of detecting such events and classifying them into AEP(definitely an Epileptiform Transient) and non-AEPs (unlikely an epileptiform transient). The problem is approached in two separate steps and cascaded to validate and analyze the performance of the overall system. The first step is a detection problem to find the Epileptiform like transients (ETs) from the Electroencephalograph (EEG) of a patient. A Radial basis function-based neural network has been trained using a training set consisting of examples from both classes (ETs and non-ETs). The ETs are the yellow boxes which are marked by expert neurologists. There are no particular examples of non-ETs and any data not annotated by experts can be considered to be examples of non-ETs. The second step is classification of the detected ETs also known as yellow boxes, into AEPs or non-AEPs. A similar Radial basis function-based neural network has been trained using the ETs marked and classified into AEPs and non-AEPs manually by seven expert neurologists. The annotations or yellow boxes along with the contextual signal was used to extract features using the Hilbert Huang Transform. The system is validated by considering an entire epoch of the patient EEG and potential ETs are identified using the detector. The potential ETs marked by the detector are classified into AEPs and non-AEPs and compared against the annotations marked by the experts