Stacked Sparse Autoencoder based Automatic Detection of Ripples and Fast Ripples in Epilepsy†

High frequency oscillations (HFOs) have been acknowledged as a putative biomarker of epileptic seizure onset zones (SOZs). Accurate detection of HFOs is significant for the preoperative localization of epileptic SOZs. In this paper, a new method is proposed to automatically detect ripples (Rs) and fast ripples (FRs) from intracranial electroencephalography (iEEG) in epilepsy. A moving-window technique is utilized to segment the filtered signals which are obtained by filtering the raw iEEG signals using two Chebyshev band-pass filters. Two stacked sparse autoencoder (SSAE) models are proposed to automatically detect Rs and FRs, respectively. By optimizing the parameters of the two SSAE models, our method yields higher sensitivity (88.9±2.4% for Rs and 83.2±2.5% for FRs) and higher specificity (92.3±2.8% for Rs and 86.1±2.8% for FRs) than other three methods do.

[1]  Justin Dauwels,et al.  Epileptiform spike detection via convolutional neural networks , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Xiongbo Wan,et al.  Fast Automatic Localization of Epileptic Seizure Onset Zones Using Complex Morlet Wavelet Transform-based Singular Value Decomposition , 2018, 2018 37th Chinese Control Conference (CCC).

[3]  Wu Min,et al.  Automatic detection of high frequency oscillations based on Fuzzy entropy and Fuzzy neural network , 2016, 2016 35th Chinese Control Conference (CCC).

[4]  Gregory K. Bergey,et al.  Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations , 2015, Clinical Neurophysiology.

[5]  Jinhua She,et al.  A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[7]  Min Wu,et al.  Fast, Accurate Localization of Epileptic Seizure Onset Zones Based on Detection of High-Frequency Oscillations Using Improved Wavelet Transform and Matching Pursuit Methods , 2017, Neural Computation.

[8]  Claudio Pollo,et al.  Electrode location and clinical outcome in hippocampal electrical stimulation for mesial temporal lobe epilepsy , 2013, Seizure.

[9]  F. Kirkham,et al.  Very good inter-rater reliability of Engel and ILAE epilepsy surgery outcome classifications in a series of 76 patients , 2011, Seizure.

[10]  Chenglin Wen,et al.  Weighted time series fault diagnosis based on a stacked sparse autoencoder , 2017 .

[11]  Su Liu,et al.  Exploring the time–frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy , 2016, Journal of neural engineering.

[12]  Jean Gotman,et al.  Interictal high-frequency oscillations (100-500 Hz) in the intracerebral EEG of epileptic patients. , 2007, Brain : a journal of neurology.

[13]  J. Gotman,et al.  A comparison between detectors of high frequency oscillations , 2012, Clinical Neurophysiology.

[14]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.

[15]  J. Sarnthein,et al.  Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis , 2014, PloS one.

[16]  Jan Cimbálník,et al.  The CS algorithm: A novel method for high frequency oscillation detection in EEG , 2018, Journal of Neuroscience Methods.

[17]  Esti Suryani,et al.  The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus , 2019, Int. J. Fuzzy Log. Intell. Syst..

[18]  Ying Liang,et al.  Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network , 2019, Front. Comput. Neurosci..

[19]  J. Jefferys,et al.  High‐frequency oscillations as a new biomarker in epilepsy , 2012, Annals of neurology.

[20]  Jinhua She,et al.  A four-stage localization method for epileptic seizure onset zones , 2017 .

[21]  J Gotman,et al.  Automatic seizure detection in SEEG using high frequency activities in wavelet domain. , 2013, Medical engineering & physics.

[22]  Yue Gao,et al.  A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy , 2018, IEEE Transactions on Medical Imaging.

[23]  Anjan Gudigar,et al.  A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images , 2019, Journal of Medical Systems.

[24]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[25]  Fabio A. González,et al.  A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection , 2013, MICCAI.