Unsupervised Seizure Detection based on Rhythmical Activity and Spike Detection in EEG Signals

In this work an unsupervised epileptic seizure detection methodology is proposed to automatically isolate candidate EEG seizure segments without requiring any apriori information or human intervention. Seizures are detected when a concentration of rhythmical activity is observed in the lower end of the EEG spectrum within the delta (≤3 Hz), theta (4–7 Hz) and alpha (8–13 Hz) frequency bands, along with an increase in EEG signal variation and a high frequency of spikes/sharp waves in the same segment. When a candidate seizure segment is detected, channel localization is also evaluated to assess its activation pattern providing feedback to the clinician regarding potential seizure onset area. The proposed methodology is evaluated using EEG signals from two open EEG datasets: the CHB-MIT Scalp EEG Database with long-term recordings and the TUH Seizure Corpus consisting of selected EEG epochs with high ictal to interictal duration ratio with an average sensitivity of 81.15-91.35% and a false detection rate between 5.33-12.15 FD/h.

[1]  Kensuke Kawai,et al.  Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images , 2019, NeuroImage: Clinical.

[2]  Saleh A. Alshebeili,et al.  A Review of EEG and MEG Epileptic Spike Detection Algorithms , 2018, IEEE Access.

[3]  Joseph Picone,et al.  The Temple University Hospital EEG Data Corpus , 2016, Front. Neurosci..

[4]  G. Lindinger,et al.  Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units , 2015, Clinical Neurophysiology.

[5]  Stephanie Gollwitzer,et al.  Automatic seizure detection in long-term scalp EEG using an adaptive thresholding technique: A validation study for clinical routine , 2014, Clinical Neurophysiology.

[6]  Dimitrios I. Fotiadis,et al.  An Unsupervised Methodology for the Detection of Epileptic Seizures Using EEG Signals: A Multi-Dataset Evaluation , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Shufang Li,et al.  Seizure Prediction Using Spike Rate of Intracranial EEG , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  K. M. Kelly,et al.  Assessment of a scalp EEG-based automated seizure detection system , 2010, Clinical Neurophysiology.

[9]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[10]  E. Halgren,et al.  Single-neuron dynamics in human focal epilepsy , 2011, Nature Neuroscience.

[11]  M. Scheuer,et al.  Seizure detection with automated EEG analysis: A validation study focusing on periodic patterns , 2014, Clinical Neurophysiology.

[12]  Ilker Yaylali,et al.  Detection of Interictal Spikes and Artifactual Data Through Orthogonal Transformations , 2005, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[13]  Dimitrios I. Fotiadis,et al.  Unsupervised detection of epileptic seizures from EEG signals: A channel-specific analysis of long-term recordings , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).