EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

scholarly journals Evaluating Invasive EEG Implantations in Medically Refractory Epilepsy with Functional Scalp EEG Recordings and Structural Imaging Data

2019 ◽  
Author(s):  
Anil Palepu ◽  
Adam Li ◽  
Zachary Fitzgerald ◽  
Katherine Hu ◽  
Julia Costacurta ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Epileptogenic Zone ◽  
Invasive Monitoring ◽  
Success Rates ◽  
Imaging Data ◽  
Localization Accuracy ◽  
Scalp Eeg ◽  
Gamma Frequency ◽  
Eeg Recordings ◽  
Medically Refractory Epilepsy

AbstractSeizures in patients with medically refractory epilepsy (MRE) epilepsy cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. The EZ is then surgically removed. Unfortunately surgical success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of the eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Important Benefit ◽  
Eeg Recordings ◽  
Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

scholarly journals Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  
Keyword(s):  
Normal Subjects ◽  
Intracranial Eeg ◽  
Zero Crossing ◽  
Temporal Precision ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Psychiatric and psychosocial morbidity 1 year after epilepsy surgery

2020 ◽  
pp. 1-8
Author(s):  
S. Patel ◽  
M. Clancy ◽  
H. Barry ◽  
N. Quigley ◽  
M. Clarke ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Surgical Treatment ◽  
Epilepsy Surgery ◽  
Refractory Epilepsy ◽  
Post Surgery ◽  
Axis I ◽  
Patients With Epilepsy ◽  
The Impact

Abstract Objectives: There is a high rate of psychiatric comorbidity in patients with epilepsy. However, the impact of surgical treatment of refractory epilepsy on psychopathology remains under investigation. We aimed to examine the impact of epilepsy surgery on psychopathology and quality of life at 1-year post-surgery in a population of patients with epilepsy refractory to medication. Methods: This study initially assessed 48 patients with refractory epilepsy using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), the Hospital Anxiety and Depression Scale (HADS) and the Quality of Life in Epilepsy Inventory 89 (QOLIE-89) on admission to an Epilepsy Monitoring Unit (EMU) as part of their pre-surgical assessment. These patients were again assessed using the SCID-I, QOLIE-89 and HADS at 1-year follow-up post-surgery. Results: There was a significant reduction in psychopathology, particularly psychosis, following surgery at 1-year follow-up (p < 0.021). There were no new cases of de novo psychosis and surgery was also associated with a significant improvement in the quality of life scores (p < 0.001). Conclusions: This study demonstrates the impact of epilepsy surgery on psychopathology and quality of life in a patient population with refractory surgery. The presence of a psychiatric illness should not be a barrier to access surgical treatment.

Manifestation of Hippocampal Interictal Discharges on Clinical Scalp EEG Recordings

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Somin Lee ◽  
Shasha Wu ◽  
James X. Tao ◽  
Sandra Rose ◽  
Peter C. Warnke ◽  
...  
Keyword(s):  
Scalp Eeg ◽  
Eeg Recordings ◽  
Interictal Discharges

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

2019 ◽  
Vol 29 (03) ◽  
pp. 1850049 ◽  
Author(s):  
Magdalena Zieleniewska ◽  
Anna Duszyk ◽  
Piotr Różański ◽  
Marcin Pietrzak ◽  
Marta Bogotko ◽  
...  
Keyword(s):  
Matching Pursuit ◽  
Disorders Of Consciousness ◽  
Sleep Architecture ◽  
Quality Of Sleep ◽  
Software Environment ◽  
Continuous Description ◽  
Matching Pursuit Decomposition ◽  
Parametric Description ◽  
Eeg Recordings

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

scholarly journals A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG

2021 ◽  
Author(s):  
Karla Burelo ◽  
Georgia Ramantani ◽  
Giacomo Indiveri ◽  
Johannes Sarnthein
Keyword(s):  
Low Power ◽  
Seizure Frequency ◽  
High Frequency ◽  
Therapy Monitoring ◽  
Non Invasive ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Eeg Recordings ◽  
Active Epilepsy ◽  
Wearable Electronic Devices

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

scholarly journals An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources

2021 ◽  
Vol 2078 (1) ◽  
pp. 012021
Author(s):  
Hongyang Zhao ◽  
Qiang Xie
Keyword(s):  
Chinese Text ◽  
Feature Fusion ◽  
Graph Model ◽  
Recall Rate ◽  
Educational Resources ◽  
Keyword Extraction ◽  
Semantic Features ◽  
Fusion Algorithm ◽  
General Semantic

Abstract In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.

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