scholarly journals EEG—Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction

2021 ◽  
Vol 11 (4) ◽  
pp. 516
Author(s):  
James Brian Romaine ◽  
Mario Pereira Martín ◽  
José Ramón Salvador Ortiz ◽  
José María Manzano Crespo
Keyword(s):  
Sensitivity And Specificity ◽  
False Positive ◽  
Single Channel ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Prediction System ◽  
Multiple Sources ◽  
Complex Issue ◽  
Instantaneous Phase ◽  
Lower Envelopes

This paper tackles the complex issue of detecting and classifying epileptic seizures whilst maintaining the total calculations at a minimum. Where many systems depend on the coupling between multiple sources, leading to hundreds of combinations of electrodes, our method calculates the instantaneous phase between non-identical upper and lower envelopes of a single-electroencephalography channel reducing the workload to the total number of electrode points. From over 600 h of simulations, our method shows a sensitivity and specificity of 100% for high false-positive rates and 83% and 75%, respectively, for moderate to low false positive rates, which compares well to both single- and multi-channel-based methods. Furthermore, pre-ictal variations in synchronisation were detected in over 90% of patients implying a possible prediction system.

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning

2021 ◽  
Author(s):  
Seungjun Ryu ◽  
Seunghyeok Back ◽  
Seongju Lee ◽  
Hyeon Seo ◽  
Chanki Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Single Channel ◽  
Seizure Detection ◽  
Detection Algorithm

scholarly journals Evaluation of a New Immunochromatography Technology Test (LDBio Diagnostics) To Detect Toxoplasma IgG and IgM: Comparison with the Routine Architect Technique

2017 ◽  
Vol 55 (12) ◽  
pp. 3395-3404 ◽  
Author(s):  
Caroline Mahinc ◽  
Pierre Flori ◽  
Edouard Delaunay ◽  
Cécile Guillerme ◽  
Sana Charaoui ◽  
...  
Keyword(s):  
Western Blot ◽  
Sensitivity And Specificity ◽  
Blot Analysis ◽  
False Positive ◽  
Automated System ◽  
University Hospitals ◽  
Content Type ◽  
Automated Screening ◽  
Commercial Kits ◽  
Fully Automated System

ABSTRACTA study comparing the ICT (immunochromatography technology)ToxoplasmaIgG and IgM rapid diagnostic test (LDBio Diagnostics, France) with a fully automated system, Architect, was performed on samples from university hospitals of Marseille and Saint-Etienne. A total of 767 prospective sera and 235 selected sera were collected. The panels were selected to test various IgG and IgM parameters. The reference technique,ToxoplasmaIgGII Western blot analysis (LDBio Diagnostics), was used to confirm the IgG results, and commercial kits Platelia Toxo IgM (Bio-Rad) and Toxo-ISAgA (bioMérieux) were used in Saint-Etienne and Marseille, respectively, as the IgM reference techniques. Sensitivity and specificity of the ICT and the Architect IgG assays were compared using a prospective panel. Sensitivity was 100% for the ICT test and 92.1% for Architect (cutoff at 1.6 IU/ml). The low-IgG-titer serum results confirmed that ICT sensitivity was superior to that of Architect. Specificity was 98.7% (ICT) and 99.8% (Architect IgG). The ICT test is also useful for detecting IgM without IgG and is both sensitive (100%) and specific (100%), as it can distinguish nonspecific IgM from specificToxoplasmaIgM. In comparison, IgM sensitivity and specificity on Architect are 96.1% and 99.6%, respectively (cutoff at 0.5 arbitrary units [AU]/ml). To conclude, this new test overcomes the limitations of automated screening techniques, which are not sensitive enough for IgG and lack specificity for IgM (rare IgM false-positive cases).

scholarly journals A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7972
Author(s):  
Jee S. Ra ◽  
Tianning Li ◽  
Yan Li
Keyword(s):  
Sensitivity And Specificity ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Optimization Method ◽  
Channel Selection ◽  
Permutation Entropy ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Epileptic Seizure Prediction

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.

scholarly journals Absence Seizure Detection Algorithm for Portable EEG Devices

2021 ◽  
Vol 12 ◽  
Author(s):  
Pawel Glaba ◽  
Miroslaw Latka ◽  
Małgorzata J. Krause ◽  
Sławomir Kroczka ◽  
Marta Kuryło ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Cost ◽  
Clinical Manifestations ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Detection Algorithm ◽  
Absence Epilepsy ◽  
Absence Seizure ◽  
Epileptiform Discharges ◽  
Morlet Continuous Wavelet Transform

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

DIAGNOSIS OF HISTOPLASMOSIS USING PRECIPITIN REACTIONS IN AGAR GEL

PEDIATRICS ◽  
1958 ◽  
Vol 22 (4) ◽  
pp. 616-627
Author(s):  
Douglas C. Heiner
Keyword(s):  
Sensitivity And Specificity ◽  
Constrictive Pericarditis ◽  
False Positive ◽  
Agar Gel ◽  
Presumptive Diagnosis ◽  
Acute Pericarditis ◽  
Cross Reactions ◽  
False Positive Reactions ◽  
Large Numbers ◽  
Immunologic Test

An easy-to-perform immunologic test for histoplasmosis is described. The test, involving precipitin reactions in agar gel, appears to have greater sensitivity and specificity than currently available tests for histoplasmosis. It is useful in the study of small or large numbers of patients. It has been possible to demonstrate at least one antigen common to H. capsulatum, B. dermatitidis, and C. immitis. This antigen may be responsible for cross reactions. Instances are cited wherein problems of cross reactions and false-positive reactions have been clarified by means of precipitin-in-gel studies. Precipitin reactions in agar have provided presumptive diagnosis of histoplasmosis in a patient with acute pericarditis, which rapidly progressed to constrictive pericarditis necessitating pericardiectomy.

EEG Trend Analysis in the ICU

2016 ◽  
pp. 209-221
Author(s):  
Amy Z. Crepeau
Keyword(s):  
Seizure Detection ◽  
Quantitative Eeg ◽  
Eeg Monitoring ◽  
Multiple Sources ◽  
Periodic Patterns ◽  
Continuous Eeg ◽  
Continuous Eeg Monitoring ◽  
It Support ◽  
Special Environment ◽  
Epilepsy Monitoring

Continuous EEG monitoring can increase the detection of subclinical seizures, and is important in managing nonconvulsive status epilepticus. In the ICU it presents challenges not routinely encountered in the outpatient EEG laboratory or the epilepsy monitoring unit: multiple sources of artifact, and the need for imaging-compatible electrodes and a robust IT support system. Rhythmic and periodic patterns of indeterminate significance are encountered. There is much debate as to the true significance of these patterns, and clinical correlation is always required. Special techniques can be employed in the application and analysis of ICU EEG monitoring. EEG has been useful in monitoring for ischemia, prognosis, and depth of medication-induced suppression. Quantitative EEG can also be utilized to assist in rapid seizure detection, and to monitor for subtle gradual changes in cerebral function and seizure detection. The special environment, however, requires close attention to technical considerations, and thoughtful interpretations of indeterminate patterns.

Pearls and Pitfalls of Interpretation in CT Colonography

2020 ◽  
Vol 71 (2) ◽  
pp. 140-148
Author(s):  
Michael Schonberger ◽  
Philippe Lefere ◽  
Abraham H. Dachman
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
False Positive ◽  
Ct Colonography ◽  
False Negative ◽  
High Sensitivity ◽  
Negative Results ◽  
False Negative Results ◽  
The Common

The accuracy of computed tomography (CT) colonography (CTC) requires that the radiologist be well trained in the recognition of pitfalls of interpretation. In order to achieve a high sensitivity and specificity, the interpreting radiologist must be well versed in the causes of both false-positive and false-negative results. In this article, we review the common and uncommon pitfalls of interpretation in CTC.

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