HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbala...

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Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 29; s. 1965 - 1976
Hlavní autoři: Wu, Min, Qin, Hongzhen, Wan, Xiongbo, Du, Yuxiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Biomarkers
Classifiers
EEG
Electroencephalography
ensemble classifier
Epilepsy
Feature extraction
Hafnium oxide
high-frequency oscillations
Machine learning
Noise reduction
Oscillations
Oscillators
sample weight adjusting factors
Seizures
Sensitivity
stacked denoising autoencoder
Support vector machines
Time-frequency analysis
Training
Weight
ISSN:1534-4320, 1558-0210, 1558-0210
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Shrnutí:High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbalance between HFOs and false HFOs, it is difficult to obtain satisfactory detection performance by the existing methods. To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors. First, the adjustable threshold of Hilbert envelopes is proposed to isolate the events of interest (EoIs) from background activities. Then, the SDAE network is utilized to automatically extract features of EoIs in the time-frequency domain. Finally, the AdaBoost-based support vector machine ensemble classifier with sample weight adjusting factors is devised to separate HFOs from EoIs by using the extracted features. These adjusting factors are used to solve the class imbalance problem by adjusting sample weights when learning the base classifiers. Our HFO detection method is evaluated by using clinical iEEG data recorded from 20 patients with medically refractory epilepsy. The experimental results show that our detection method outperforms some existing methods in terms of sensitivity and false discovery rate. In addition, the HFOs detected by our method are effective for localizing seizure onset zones.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2021.3113293