An extensive review on development of EEG-based computer-aided diagnosis systems for epilepsy detection

Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning—clinical application perspectives

The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.

A progressive deep wavelet cascade classification model for epilepsy detection

Automatic epileptic seizure detection according to EEG recordings is helpful for neurologists to identify an epilepsy occurrence in the initial anti-epileptic treatment. To quickly and accurately detect epilepsy, we proposed a progressive deep wavelet cascade classification model (PDWC) based on the discrete wavelet transform (DWT) and Random Forest (RF). Different from current deep networks, the PDWC mimics the progressive object identification process of human beings with recognition cycles. In every cycle, enhanced wavelet energy features at a specific scale were extracted by DWT and input into a set of cascade RF classifiers to realize one recognition. The recognition accuracy of PDWC is gradually improved by the fusion of classification results produced by multiple recognition cycles. Moreover, the cascade structure of PDWC can be automatically determined by the classification accuracy increment between layers. To verify the performance of the PDWC, we respectively applied five traditional schemes and four deep learning schemes to four public datasets. The results show that the PDWC is not only superior than five traditional schemes, including KNN, Bayes, DT, SVM, and RF, but also better than deep learning methods, i.e. convolutional neural network (CNN), Long Short-Term Memory (LSTM), multi-Grained Cascade Forest (gcForest) and wavelet cascade model (WCM). The mean accuracy of PDWC for all subjects of all datasets reaches to 0.9914. With a flexible structure and less parameters, the PDWC is more suitable for the epilepsy detection of diverse EEG signals.

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

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Complexity measures are formulated to enhance classical time-domain statistics of EEG

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The detection algorithm does not need ad-hoc data preprocessing to address artifacts

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Focal seizures are detected 95% of the time with less than four false alarms per day

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The approach offers a visual representation of a seizure as a time-evolving network

Epilepsy classification using optimized artificial neural network

OBJECTIVES

An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process.

MATERIALS

In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point.

RESULTS

For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification.

DISCUSSION

Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction.

ABBREVIATIONS

EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.