Scalpnet: Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Using Convolutional Neural Networks

We propose ScalpNet: A deep neural network to detect spatiotemporal abnormal intervals from EEGs of epilepsy patients. Since the number of trained clinicians is very limited, it is very crucial to establish automatic detection of abnormal signals caused by epilepsy from EEGs. We build a convolutional neural network detecting spatiotemporal intervals that will be abnormal based on the fact that peaky EEG signals can be observed not only in the electrode close to the focal region but those in the surrounding regions. In the experiments with a real dataset, our proposed ScalpNet presents higher classification accuracy than existing machine learning methods, including a convolutional neural network performed by channel-by-channel.

[1]  Philippe Ryvlin,et al.  Prevention of sudden unexpected death in epilepsy: A realistic goal? , 2013, Epilepsia.

[2]  Fathi E. Abd El-Samie,et al.  EEG seizure detection and prediction algorithms: a survey , 2014, EURASIP J. Adv. Signal Process..

[3]  D. Nair,et al.  Epilepsy , 1977, Journal of Neurology.

[4]  Edward Y. Chang,et al.  KBA: kernel boundary alignment considering imbalanced data distribution , 2005, IEEE Transactions on Knowledge and Data Engineering.

[5]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Lei Wang,et al.  Seizure pattern-specific epileptic epoch detection in patients with intellectual disability , 2017, Biomed. Signal Process. Control..

[7]  Rubén San-Segundo-Hernández,et al.  Classification of epileptic EEG recordings using signal transforms and convolutional neural networks , 2019, Comput. Biol. Medicine.

[8]  Seetha Hari,et al.  Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.

[9]  Tanaka Toshihisa,et al.  Detecting Abnormal Section in Epileptic EEG Using Deep Neural Networks , 2019 .

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

[11]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[12]  Jasmin Kevric,et al.  Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction , 2018, Biomed. Signal Process. Control..

[13]  Joelle Pineau,et al.  Learning Robust Features using Deep Learning for Automatic Seizure Detection , 2016, MLHC.

[14]  Toshihisa Tanaka,et al.  Fully Data-driven Convolutional Filters with Deep Learning Models for Epileptic Spike Detection , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[16]  Subhrajit Roy,et al.  Deep Learning Enabled Automatic Abnormal EEG Identification , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[18]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[20]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..