A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy

High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examinati...

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Vydáno v:	IEEE transactions on medical imaging Ročník 37; číslo 11; s. 2474 - 2482
Hlavní autoři:	Guo, Jiayang, Yang, Kun, Liu, Hongyi, Yin, Chunli, Xiang, Jing, Li, Hailong, Ji, Rongrong, Gao, Yue
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adolescent Adult Algorithms Background noise Biomarkers brain Brain - diagnostic imaging Child Deep Learning deep learning model detector Detectors Epilepsy Epilepsy - diagnostic imaging Error detection Feature extraction Female Hafnium oxide Head High-frequency oscillations Humans Image Interpretation, Computer-Assisted - methods Machine learning Magnetoencephalography Magnetoencephalography - methods Male MEG Middle Aged Optimization Oscillations Performance measurement Sensitivity and Specificity Sensors Signal Processing, Computer-Assisted SSAE Surgery Young Adult
ISSN:	0278-0062, 1558-254X, 1558-254X
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Shrnutí:	High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0278-0062 1558-254X 1558-254X
DOI:	10.1109/TMI.2018.2836965