Recognition of interictal and ictal discharges on EEG. Focal vs generalized epilepsy

Andrade-Machado, René; Cuartas, Vanesa. Benjumea; Muhammad, Irshad Khan.

doi:10.1016/j.yebeh.2021.107830

Cited by 17 publications

(14 citation statements)

References 65 publications

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“…Photosensitivity is observed in 30% of patients. Although focal and generalized epilepsies can coexist, it is quite rare 30 …”

Section: Long-term Eeg Monitoringmentioning

confidence: 99%

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EEG Essentials

Tatum¹

2022

CONTINUUM: Lifelong Learning in Neurology

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PURPOSE OF REVIEW: EEG is the best study for evaluating the electrophysiologic function of the brain. The relevance of EEG is based on an accurate interpretation of the recording. Understanding the neuroscientific basis for EEG is essential. The basis for recording and interpreting EEG is both brain site-specific and technique-dependent to detect and represent a complex series of waveforms. Separating normal from abnormal EEG lies at the foundation of essential interpretative skills.RECENT FINDINGS: Seizures and epilepsy are the primary targets for clinical use of EEG in diagnosis, seizure classification, and management. Interictal epileptiform discharges on EEG support a clinical diagnosis of seizures, but only when an electrographic seizure is recorded is the diagnosis confirmed. New variations of normal waveforms, benign variants, and artifacts can mimic epileptiform patterns and are potential pitfalls for misinterpretation for inexperienced interpreters. A plethora of medical conditions involve nonepileptiform and epileptiform abnormalities on EEG along the continuum of people who appear healthy to those who are critically ill. Emerging trends in long-term EEG monitoring to diagnose, classify, quantify, and characterize patients with seizures have unveiled epilepsy syndromes in patients and expanded medical and surgical options for treatment. Advances in terminology and application of continuous EEG help unify neurologists in the diagnosis of nonconvulsive seizures and status epilepticus in patients with encephalopathy and prognosticate recovery from serious neurologic injury involving the brain. SUMMARY: After 100 years, EEG has retained a key role in the neurologist's toolkit as a safe, widely available, versatile, portable test of neurophysiology, and it is likely to remain at the forefront for patients with neurologic diseases. Interpreting EEG is based on qualitative review, and therefore, the accuracy of reporting is based on the interpreter's training, experience, and exposure to many new and older waveforms.

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“…Photosensitivity is observed in 30% of patients. Although focal and generalized epilepsies can coexist, it is quite rare 30 …”

Section: Long-term Eeg Monitoringmentioning

confidence: 99%

“…Although focal and generalized epilepsies can coexist, it is quite rare. 30 FIGURE 2-37 Subclinical seizure (arrows) due to a left hemispheric intraparenchymal hemorrhage in a 59-year-old comatose man. Evolution was subtle and enhanced by using a 15-mm/s (slow) display speed.…”

Section: Long-term Eeg Monitoringmentioning

confidence: 99%

EEG Essentials

Tatum¹

2022

CONTINUUM: Lifelong Learning in Neurology

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“…Slowly real time clinical diagnostics is tried out [43]. But most of the literature available classification is done in ictal stage EEG signal and very less exploration in inter ictal or other stages can be found because of its similarity with nonepileptic EEG [13, 44,45].…”

Section: Introductionmentioning

confidence: 99%

Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages using Machine Learning Techniques

2023

AMLAI

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Background: Epileptic Seizure type diagnosis is done by clinician based on the symptoms during the episode and the Electroencephalograph (EEG) recording taken during inter-ictal period. But main challenge is, most of the time with the absence of any attendee, the patients are unable to explain the symptoms and not possible to find signature in inter-ictal EEG signal. Aims: This paper aims to analyze epileptic seizure Electro-encephalograph (EEG) signals to diagnose seizure in pre-ictal, ictal and inter-ictal stages and to classify into seven different classes. Methods: Temple University Hospital licensed dataset is used for study. From the seizure corpus, seven seizure types are pre- processed and segregated into pre-ictal, ictal and inter-ictal stages. The multi class classification performed using different machine and deep learning techniques such as K- Nearest Neighbor (KNN) and Random Forest, etc. Results: Multiclass classification of seven type of epileptic seizure with 20 channels, with 80-20 train-test ratio, is achieved 94.7%, 94.7%, 69.0% training accuracy and 94.46%, 94.46% 71.11% test accuracy by weighted KNN for pre-ictal, ictal and inter-ictal stages respectively. Conclusion: Seven epileptic seizure type classification using machine learning techniques carried out with MATLAB software and weighted KNN shows better accuracy comparatively.

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“…Physiological signals: in particular, EEG signals are most often used for emotion recognition [ 6 , 7 ]. EEG signals have been widely used to study swallowing, analyze mental states, and assist in diagnosing neuropsychiatric diseases [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology

Liu

Gao

et al. 2022

Computational Intelligence and Neuroscience

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With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students’ emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.

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Recognition of interictal and ictal discharges on EEG. Focal vs generalized epilepsy

Cited by 17 publications

References 65 publications

EEG Essentials

EEG Essentials

Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages using Machine Learning Techniques

A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology

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