Comput. Biol. Med. - An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionality reduction.

Tópicos

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Resumo

Epileptic seizure features always include the morphology and spatial distribution of nonlinear waveforms in the electroencephalographic (EEG) signals. In this study, we propose a novel incremental learning scheme based on nonlinear dimensionality reduction for automatic patient-specific seizure onset detection. The method allows for identification of seizure onset times in long-term EEG signals acquired from epileptic patients. Firstly, a nonlinear dimensionality reduction (NDR) method called local tangent space alignment (LTSA) is used to reduce the dimensionality of available initial feature sets extracted with continuous wavelet transform (CWT). One-dimensional manifold which reflects the intrinsic dynamics of seizure onset is obtained. For each patient, IEEG recordings containing one seizure onset is sufficient to train the initial one-dimensional manifold. Secondly, an unsupervised incremental learning scheme is proposed to update the initial manifold when the unlabelled EEG segments flow in sequentially. The incremental learning scheme can cluster the new coming samples into the trained patterns (containing or not containing seizure onsets). Intracranial EEG recordings from 21 patients with duration of 193.8h and 82 seizures are used for the evaluation of the method. Average sensitivity of 98.8%, average uninteresting false positive rate of 0.24/h, average interesting false positives rate of 0.25/h, and average detection delay of 10.8s are obtained. Our method offers simple, accurate training with less human intervening and can be well used in off-line seizure detection. The unsupervised incremental learning scheme has the potential in identifying novel IEEG classes (different onset patterns) within the data.

Resumo Limpo

epilept seizur featur alway includ morpholog spatial distribut nonlinear waveform electroencephalograph eeg signal studi propos novel increment learn scheme base nonlinear dimension reduct automat patientspecif seizur onset detect method allow identif seizur onset time longterm eeg signal acquir epilept patient first nonlinear dimension reduct ndr method call local tangent space align ltsa use reduc dimension avail initi featur set extract continu wavelet transform cwt onedimension manifold reflect intrins dynam seizur onset obtain patient ieeg record contain one seizur onset suffici train initi onedimension manifold second unsupervis increment learn scheme propos updat initi manifold unlabel eeg segment flow sequenti increment learn scheme can cluster new come sampl train pattern contain contain seizur onset intracrani eeg record patient durat h seizur use evalu method averag sensit averag uninterest fals posit rate h averag interest fals posit rate h averag detect delay s obtain method offer simpl accur train less human interven can well use offlin seizur detect unsupervis increment learn scheme potenti identifi novel ieeg class differ onset pattern within data

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