BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

Xu, Zhendi; Wang, Tianlei; Cao, Jiuwen; Bao, Zihang; Jiang, Tiejia; Gao, Feng

doi:10.1109/tnsre.2021.3107142

Cited by 41 publications

(32 citation statements)

References 46 publications

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“…Two studies included private datasets on benign rolandic epilepsy with centrotemporal spikes (BECTS), which is a type of focal epilepsy seen only in children. 24, 25 Generalized epilepsy datasets in existing work are also private and only consist of EEG recordings from patients with idiopathic generalized epilepsy. 2, 3, 26 TUEV 13 is the largest public dataset containing both focal and generalised epileptiform activities with different EEG recording settings.…”

Section: Related Workmentioning

confidence: 99%

Automated Interictal Epileptiform Discharge Detection From Scalp EEG Using Scalable Time-series Classification Approaches

Nhu

Janmohamed

Shakhatreh

et al. 2022

Preprint

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Deep learning for automated interictal epileptiform discharge (IED) detection has been topical with many published papers in recent years. All existing work viewed EEG signals as time-series and developed specific models for IED classification; however, general time-series classification (TSC) methods were not considered. Moreover, none of these methods were evaluated on any public datasets, making direct comparisons challenging. This paper explored two state-of-the-art convolutional-based TSC algorithms, InceptionTime and Minirocket, on IED detection. We fine-tuned and cross-evaluated them on two private and public (Temple University Events - TUEV) datasets and provided ready metrics for benchmarking future work. We observed that the optimal parameters correlated with the clinical duration of an IED and achieved the best AUC, AUPRC and F1 scores of 0.98, 0.80 and 0.77 on the private datasets, respectively. The AUC, AUPRC and F1 on TUEV were 0.99, 0.99 and 0.97, respectively. While algorithms trained on the private sets maintained the performance when tested on the TUEV data, those trained on TUEV could not generalise well to the private data. These results emerge from differences in the class distributions across datasets and indicate a need for public datasets with a better diversity of IED waveforms, background activities and artifacts to facilitate standardisation and benchmarking of algorithms.

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Section: Related Workmentioning

confidence: 99%

Automated Interictal Epileptiform Discharge Detection From Scalp EEG Using Scalable Time-series Classification Approaches

Nhu

Janmohamed

Shakhatreh

et al. 2022

Preprint

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“…These models help increment of EEG features to additionally foster request precision for different clinical applications. Equivalent models are discussed in [17,18], wherein Extended K Nearest Neighbors, and Joint outwardly impeded source division systems are proposed by researchers for better adaptability execution. These models utilize low multifaceted nature feature extraction systems, yet can't be applied to gigantic extension EEG datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of an improved soft computing model for EEG classification using MFCC and quad classifier

Kala¹,

Mishra²,

Dubey

2022

ijhs

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Electroencephalography (EEG) signals are a combination of complex pattern sequences,that are periodic in nature. These pattern sequences include a gamma waves that indicates deep thinking behaviour, a beta wave sequence that indicates busy and active mind status, an alpha wave segment which indicates reflective and restful behaviour, a theta wave which is an indicative of drowsiness, and a delta wave which indicates sleeping & dreaming conditions. Features like frequency changes, amplitude changes, pattern changes, etc. are used to identify chronic, ischemic and other diseases related to the brain. In order to classify these wave patterns into brain diseases like epilepsy, a series of high complexity signal processing operations are needed to be executed in tandem. These operations include signal pre-processing, feature extraction, feature selection, classification into epileptic & non-epileptic seizure and post-processing.A large variety of algorithms are developed by researchers for each of these operations. Performance of these algorithms varies largely w.r.t. the number of leads used for EEG capture, filtering efficiency, feature extraction & selection efficiency, and classifier efficiency.Thus, it becomes ambiguous for researchers and system designers to select the best possible algorithm set for their application. the proposed AMVAFEx model uses Multiple Neural Networks for definite EEG grouping.

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“…E YE blink artifact detection and elimination are important in EEG analysis [1], [2], particularly for EEG-based nervous system diseases analysis [3]- [7], such as epilepsy, cerebellitis, etc. Eye blinks are generally caused by blinking or eye movement, and are mainly obvious in the front of the scalp.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges

Wang

Cui

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objectives: Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. Methods: In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multidimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. Results: Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medi-Manuscript

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BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

Cited by 41 publications

References 46 publications

Automated Interictal Epileptiform Discharge Detection From Scalp EEG Using Scalable Time-series Classification Approaches

Automated Interictal Epileptiform Discharge Detection From Scalp EEG Using Scalable Time-series Classification Approaches

Design of an improved soft computing model for EEG classification using MFCC and quad classifier

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges

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