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Deep multi-view feature learning for EEG-based epileptic seizure detection.

08:00 EDT 11th September 2019 | BioPortfolio

Summary of "Deep multi-view feature learning for EEG-based epileptic seizure detection."

Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By analyzing the encephalogram (EEG) signals of patients with epilepsy, their conditions can be monitored and seizure can be detected and intervened in time. As the identification of effective features in EEG signals is important for accurate seizure detection, this paper proposes a multi-view deep feature extraction method in attempt to achieve this goal. The method first uses fast Fourier transform (FFT) and wavelet packet decomposition (WPD) to construct the initial multi-view features. Convolutional neural network (CNN) is then used to automatically learn deep features from the initial multi-view features, which reduces the dimensionality and obtain the features with better seizure identification ability. Furthermore, the multi- view Takagi-Sugeno-Kang fuzzy system (MV-TSK-FS), an interpretable rule-based classifier, is used to construct a classification model with strong generalizability based on the deep multi- view features obtained. Experimental studies show that the classification accuracy of the proposed multi-view deep feature extraction method is at least 1% higher than that of common feature extraction methods such as principal component analysis (PCA), FFT and WPD. The classification accuracy is also at least 4% higher than the average accuracy achieved with single-view deep features.

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This article was published in the following journal.

Name: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
ISSN: 1558-0210
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Medical and Biotech [MESH] Definitions

Recurrent conditions characterized by epileptic seizures which arise diffusely and simultaneously from both hemispheres of the brain. Classification is generally based upon motor manifestations of the seizure (e.g., convulsive, nonconvulsive, akinetic, atonic, etc.) or etiology (e.g., idiopathic, cryptogenic, and symptomatic). (From Mayo Clin Proc, 1996 Apr;71(4):405-14)

Conditions characterized by recurrent paroxysmal neuronal discharges which arise from a focal region of the brain. Partial seizures are divided into simple and complex, depending on whether consciousness is unaltered (simple partial seizure) or disturbed (complex partial seizure). Both types may feature a wide variety of motor, sensory, and autonomic symptoms. Partial seizures may be classified by associated clinical features or anatomic location of the seizure focus. A secondary generalized seizure refers to a partial seizure that spreads to involve the brain diffusely. (From Adams et al., Principles of Neurology, 6th ed, pp317)

A disorder characterized by recurrent episodes of paroxysmal brain dysfunction due to a sudden, disorderly, and excessive neuronal discharge. Epilepsy classification systems are generally based upon: (1) clinical features of the seizure episodes (e.g., motor seizure), (2) etiology (e.g., post-traumatic), (3) anatomic site of seizure origin (e.g., frontal lobe seizure), (4) tendency to spread to other structures in the brain, and (5) temporal patterns (e.g., nocturnal epilepsy). (From Adams et al., Principles of Neurology, 6th ed, p313)

Epileptic condition in which adequate trials of two tolerated and appropriately chosen and used ANTIEPILEPTIC DRUGS schedules to achieve sustained seizure freedom failed.

Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)

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