Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG

2015 ◽  
Vol 25 (06) ◽  
pp. 1550020 ◽  
Author(s):  
Yanli Zhang ◽  
Weidong Zhou ◽  
Shasha Yuan
Keyword(s):  
Multifractal Analysis ◽  
Detection System ◽  
Transient Behavior ◽  
Seizure Detection ◽  
Relevance Vector Machine ◽  
Detection Technology ◽  
Ictal Eeg ◽  
Interictal Eeg ◽  
Eeg Recordings ◽  
Event Based

Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of this work is to develop a seizure detection system with high accuracy. The proposed system was mainly based on multifractal analysis, which describes the local singular behavior of fractal objects and characterizes the multifractal structure using a continuous spectrum. Compared with computing the single fractal dimension, multifractal analysis can provide a better description on the transient behavior of EEG fractal time series during the evolvement from interictal stage to seizures. Thus both interictal EEG and ictal EEG were analyzed by multifractal formalism and their differences in the multifractal features were used to distinguish the two class of EEG and detect seizures. In the proposed detection system, eight features (α0, αmin, αmax, Δα, f(α min ), f(α max ), Δf and R) were extracted from the multifractal spectrums of the preprocessed EEG to construct feature vectors. Subsequently, relevance vector machine (RVM) was applied for EEG patterns classification, and a series of post-processing operations were used to increase the accuracy and reduce false detections. Both epoch-based and event-based evaluation methods were performed to appraise the system's performance on the EEG recordings of 21 patients in the Freiburg database. The epoch-based sensitivity of 92.94% and specificity of 97.47% were achieved, and the proposed system obtained a sensitivity of 92.06% with a false detection rate of 0.34/h in event-based performance assessment.

Using Dictionary Pair Learning for Seizure Detection

2019 ◽  
Vol 29 (04) ◽  
pp. 1850005 ◽  
Author(s):  
Xin Ma ◽  
Nana Yu ◽  
Weidong Zhou
Keyword(s):  
Detection Rate ◽  
Recognition Rate ◽  
Seizure Detection ◽  
False Detection Rate ◽  
False Detection ◽  
Minimization Method ◽  
System A ◽  
Training Samples ◽  
Eeg Recordings ◽  
Event Based

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

scholarly journals P.052 Benign spasms of infancy - a mimicker of infantile epileptic disorders

2019 ◽  
Vol 46 (s1) ◽  
pp. S28
Author(s):  
J Ghossein ◽  
D Pohl
Keyword(s):  
Epileptiform Activity ◽  
Positive Family History ◽  
Myoclonic Epilepsy ◽  
First Year ◽  
Neurodevelopmental Outcomes ◽  
Epileptic Spasms ◽  
Ictal Eeg ◽  
Interictal Eeg ◽  
Eeg Recordings ◽  
First Year Of Life

Background: Benign spasms of infancy (BSI), previously described as benign non-epileptic infantile spasms or benign myoclonus of early infancy, are non-epileptic movements manifesting during the first year of life and spontaneously resolving in the second year of life. BSI are characterized by spasms typically lasting 1-2 seconds, involving to varying degrees the head, neck, trunk, shoulders and upper extremities. Ictal and interictal EEG recordings are normal. BSI are not associated with developmental retardation and do not require treatment. Distinction between BSI and infantile epileptic disorders, such as epileptic spasms or myoclonic epilepsy of infancy, can be challenging given the clinical similarities. Moreover, interictal EEGs can be normal in all conditions. Epileptic spasms and myoclonic epilepsy require timely treatment to improve neurodevelopmental outcomes. Methods: We describe a 6-month old infant presenting with spasm-like movements. His paroxysms as well as a positive family history for epileptic spasms were in keeping with a likely diagnosis of West syndrome. Results: Surprisingly, ictal video EEG did not reveal epileptiform activity, and suggested a diagnosis of BSI. Conclusions: We emphasize that ictal EEG is the gold standard for classification of infantile paroxysms as either epileptic or non-epileptic, thereby avoiding overtreatment of BSI and facilitating timely targeted treatment of infantile epilepsies.

scholarly journals Epileptic Seizure Detection Using Artifact Reduction and HOS Features of WPD

2020 ◽  
Vol 32 ◽  
pp. 02008
Author(s):  
Meenal Vijay Kakade ◽  
Chandrakant J. Gaikwad ◽  
Vijay R. Dahake
Keyword(s):  
Signal Processing ◽  
Video Processing ◽  
Detection System ◽  
Wavelet Packet ◽  
Seizure Detection ◽  
Detection Algorithms ◽  
Analysis Process ◽  
Improved Performance ◽  
Eeg Recordings ◽  
Processing Techniques

The use of computer aided diagnosis systems for disease identifiscation, based on signal processing, image processing and video processing terminologies is common due to emerging technologies in medical field. The detection of epilepsy seizures using EEG recordings is done by different signal processing techniques. To reduce the disability caused by the uncertainty of the occurrence of seizures, a recording system which shall result accurate and early detection of seizure with quick warning is greatly desired. To optimize the performance of EEG based epilepsy seizures detection, in this work we are presenting a method based on two key algorithms. Here, we propose unique algorithm based on SWT (Stationary Wavelet Transform), for easier seizure analysis process, along with improved performance of the application of seizure detection algorithms. Then, we propose the algorithm for feature extraction that makes use of Higher Order Statistics of the coefficients that are calculated using Wavelet Packet Decomposition (WPD).This helps in improving the epilepsy seizures detection performance. The proposed methods helps to improve the overall efficiency and robustness of EEG based epilepsy seizures detection system.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection

2016 ◽  
Vol 26 (01) ◽  
pp. 1550035 ◽  
Author(s):  
Junhui Li ◽  
Weidong Zhou ◽  
Shasha Yuan ◽  
Yanli Zhang ◽  
Chengcheng Li ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Seizure Detection ◽  
Elastic Net ◽  
Patient Specific ◽  
Specific Method ◽  
Training Samples ◽  
Learned Dictionary ◽  
Average Latency ◽  
Eeg Recordings ◽  
Event Based

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Kernel Collaborative Representation-Based Automatic Seizure Detection in Intracranial EEG

2015 ◽  
Vol 25 (02) ◽  
pp. 1550003 ◽  
Author(s):  
Shasha Yuan ◽  
Weidong Zhou ◽  
Qi Yuan ◽  
Xueli Li ◽  
Qi Wu ◽  
...  
Keyword(s):  
Detection Rate ◽  
Detection System ◽  
High Sensitivity ◽  
Seizure Detection ◽  
Least Square ◽  
Collaborative Representation ◽  
False Detection Rate ◽  
False Detection ◽  
Training Samples ◽  
Eeg Recordings

Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automatic seizure detection in intracranial electroencephalogram (iEEG) recordings based on kernel collaborative representation (KCR). Firstly, the EEG recordings are divided into 4s epochs, and then wavelet decomposition with five scales is performed. After that, detail signals at scales 3, 4 and 5 are selected to be sparsely coded over the training sets using KCR. In KCR, l2-minimization replaces l1-minimization and the sparse coefficients are computed with regularized least square (RLS), and a kernel function is utilized to improve the separability between seizure and nonseizure signals. The reconstructed residuals of each EEG epoch associated with seizure and nonseizure training samples are compared and EEG epochs are categorized as the class that minimizes the reconstructed residual. At last, a multi-decision rule is applied to obtain the final detection decision. In total, 595 h of iEEG recordings from 21 patients with 87 seizures are employed to evaluate the system. The average sensitivity of 94.41%, specificity of 96.97%, and false detection rate of 0.26/h are achieved. The seizure detection system based on KCR yields both a high sensitivity and a low false detection rate for long-term EEG.

scholarly journals Electroencephalographic Features of Temporal Lobe Epilepsy

2010 ◽  
Vol 37 (4) ◽  
pp. 439-448 ◽  
Author(s):  
Mohammed M. Jan ◽  
Mark Sadler ◽  
Susan R. Rahey
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Surgical Management ◽  
Temporal Lobe ◽  
Seizure Onset ◽  
Resective Surgery ◽  
Accurate Localization ◽  
Epileptiform Discharges ◽  
Ictal Eeg ◽  
Temporal Lobe Seizures ◽  
Eeg Recordings

Electroencephalography (EEG) is an important tool for diagnosing, lateralizing and localizing temporal lobe seizures. In this paper, we review the EEG characteristics of temporal lobe epilepsy (TLE). Several “non-standard” electrodes may be needed to further evaluate the EEG localization, Ictal EEG recording is a major component of preoperative protocols for surgical consideration. Various ictal rhythms have been described including background attenuation, start-stop-start phenomenon, irregular 2-5 Hz lateralized activity, and 5-10 Hz sinusoidal waves or repetitive epileptiform discharges. The postictal EEG can also provide valuable lateralizing information. Postictal delta can be lateralized in 60% of patients with TLE and is concordant with the side of seizure onset in most patients. When patients are being considered for resective surgery, invasive EEG recordings may be needed. Accurate localization of the seizure onset in these patients is required for successful surgical management.

Electroencephalographic Recordings of Focal Seizures in Patients Affected by Periventricular Nodular Heterotopia: Role of the Heterotopic Nodules in the Genesis of Epileptic Discharges

2004 ◽  
Vol 19 (3) ◽  
pp. 369-377
Author(s):  
Giorgio Battaglia ◽  
Silvana Franceschetti ◽  
Luisa Chiapparini ◽  
Elena Freri ◽  
Stefania Bassanini ◽  
...  
Keyword(s):  
Brain Regions ◽  
Periventricular Nodular Heterotopia ◽  
Focal Seizures ◽  
Ictal Eeg ◽  
Epileptic Discharges ◽  
Nodular Heterotopia ◽  
Eeg Recordings ◽  
Drug Resistant Epilepsy ◽  
Video Eeg

Patients affected by periventricular nodular heterotopia are frequently characterized by focal drug-resistant epilepsy. To investigate the role of periventricular nodules in the genesis of seizures, we analyzed the electroencephalographic (EEG) features of focal seizures recorded by means of video-EEG in 10 patients affected by different types of periventricular nodular heterotopia and followed for prolonged periods of time at the epilepsy center of our institute. The ictal EEG recordings with surface electrodes revealed common features in all patients: all seizures originated from the brain regions where the periventricular nodular heterotopia were located; EEG patterns recorded on the leads exploring the periventricular nodular heterotopia were very similar both at the onset and immediately after the seizure's end in all patients. Our data suggest that seizures are generated by abnormal anatomic circuitries, including the heterotopic nodules and adjacent cortical areas. The major role of heterotopic neurons in the genesis and propagation of epileptic discharges must be taken into account when planning surgery for epilepsy in patients with periventricular nodular heterotopia. ( J Child Neurol 2005;20:369—377).

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