Novel deep learning framework for detection of epileptic seizures using EEG signals.

Introduction: Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals have emerged as a valuable tool in detecting epilepsy and other neurological disorders.

Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics.

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics.

Pitfalls in scalp EEG: Current obstacles and future directions.

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG.

The International Cardiac Arrest Research Consortium Electroencephalography Database.

Objectives: To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest.

Design: Multicenter cohort, partly prospective and partly retrospective.

Setting: Seven academic or teaching hospitals from the United States and Europe.

Tagging EEG features within exam reports to quickly generate databases for research purposes.

Objective: assess the effectiveness of a new method for classifying EEG recording features through the use of tags within reports. We present feature prevalence in a sample of patients with toxic-metabolic encephalopathy and discuss the advantages of this approach over existing classification systems.

Methods: during EEG report creation, tags reflecting background activity, epileptiform features and periodic discharges were selected according to the findings of each recording.

The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database.

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest.

Design: Multicenter cohort, partly prospective and partly retrospective.

Setting: Seven academic or teaching hospitals from the U.

Normal variants and artifacts: Importance in EEG interpretation.

Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal.

Wired for sound: The effect of sound on the epileptic brain.

Sound waves are all around us resonating at audible and inaudible frequencies. Our ability to hear is crucial in providing information and enabling interaction with our environment. The human brain generates neural oscillations or brainwaves through synchronised electrical impulses.

Spectrum of Electroencephalography Findings in Newly Diagnosed Epilepsy.

Background Epilepsy is a neurological disorder that presents with recurrent seizures associated with erratic brain activity which can be measured through EEG in addition to other neurological investigations. However, EEG may show abnormal patterns and waveforms while the patient is having a seizure which is crucial for making an accurate diagnosis. Objective This study aims to evaluate the spectrum of EEG findings in newly diagnosed epileptic patients as part of a neurological investigation.

Can We Distinguish Triphasic Waves From Other Generalized Periodic Discharges? Do We Need to?

Triphasic waves are intuitively distinctive waveforms that fall under the umbrella of generalized periodic discharges. The ability to distinguish these waveforms consistently could be helpful if a specific underlying pathophysiology could be identified. However, scalp EEG and clinical observation have been limited in their ability to elucidate the underlying cortical physiology that leads to triphasic waves.

Reward mechanism of depressive episodes in bipolar disorder: Enhanced theta power in feedback-related negativity.

Introduction: This study aimed to explore the reward-related neural mechanism in patients with depressive mood in bipolar disorder (BD) using event-related potentials. It remains unknown whether or not different neurobiological markers underlying depression symptoms in BD depression and major depression disorder (MDD).

Methods: 24 patients with BD depression and 20 healthy controls were included.

Lateralized Periodic Discharges in a Patient With Dural Arteriovenous Fistula: SPECT and DWI Studies Suggest They are Ictal.

Lateralized periodic discharges (LPDs) are unilateral electroencephalography (EEG) waveforms, recurring at regular intervals. There has been a long-lasting debate about whether they represent ictal or interictal phenomena. Very few patients in the literature have been investigated with multimodal functional imaging techniques.

Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis.

Interictal epileptiform discharges (IEDs) are elicited from an epileptic brain, whereas they can also be due to other neurological abnormalities. The diversity in their morphologies, their strengths, and their sources within the brain cause a great deal of uncertainty in their labeling by clinicians. The aim of this study is therefore to exploit and incorporate this uncertainty (the probability of the waveform being an IED) in the IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method.

[Fecal microbiota transplantation regulates the cholinergic anti-inflammatory pathway in cerebral cortex of septic rats through intestinal microbiota].

Objective: To investigate the effects of fecal microbiota transplantation on septic gut flora and the cortex cholinergic anti-inflammatory pathway in rats.

Methods: Sixty clean grade male Sprague-Dawley (SD) rats were divided into normal saline (NS) control group, sepsis model group and fecal microbiota transplantation group by random number table, with 20 rats in each group. The rat model of sepsis was reproduced by injection of 10 mg/kg lipopolysaccharide (LPS) via tail vein, the rats in the NS control group was given the same amount of NS.

A multi-context learning approach for EEG epileptic seizure detection.

Background: Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroencephalogram (EEG) is the most commonly used biological measurement to effectively capture the abnormalities of different brain areas during the EEG seizures.

EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection.

Diagnosis of epilepsy based on visual inspection of electroencephalogram (EEG) abnormalities is an inefficient, time-consuming, and expert-centered process. Moreover, the diagnosis based on ictal epileptiform events is challenging as the ictal patterns are infrequent. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential.

Association Between Positive Occipital Sharp Transients of Sleep and Lambda Waves.

Positive occipital sharp transients of sleep (POSTS) and lambda waves have similar morphology and location. We studied a possible association between these 2 normal EEG patterns. We reviewed a series of consecutive unselected ambulatory EEGs during a 3-month period (October 16, 2017 to January 19, 2018) and identified records with POSTS and records with lambda waves.

The Texting Rhythm: A Novel EEG Waveform Using Smartphones.

Introduction: We report a unique EEG phenomenon in patients with paroxysmal neurological events undergoing video EEG monitoring.

Methods: Two epilepsy centers analyzed the interictal scalp EEG in patients using personal electronic devices during epilepsy monitoring. The texting rhythm (TR) was defined as a reproducible, stimulus-evoked, generalized frontocentral monomorphic burst of 5-6 Hz theta consistently induced by active text messaging.

Ischemic injury suppresses hypoxia-induced electrographic seizures and the background EEG in a rat model of perinatal hypoxic-ischemic encephalopathy.

The relationship among neonatal seizures, abnormalities of the electroencephalogram (EEG), brain injury, and long-term neurological outcome (e.g., epilepsy) remains controversial.

Assessment of EEG as a Diagnostic and Prognostic Indicator Tool in the Febrile Seizures.

Objective: To assess the sensitivity, specificity, and predictive value of EEG as a diagnostic and prognostic tool in the febrile seizures.

Method: This study was conducted on 50 consecutive children with febrile seizures attending the pediatric OPD of a tertiary care hospital. The children were prospectively identified and EEG was carried out on two occasions.

Source localization of an event-related potential marker of executive attention following mild traumatic brain injury.

Recent research suggests that intact performance on an executive attention task after mild traumatic brain injury (mTBI) reflects functional adaptation within neural networks, rather than recovery of premorbid modes of information processing. However, it is unclear whether this compensation includes the recruitment of alternative neural processing resources. The current study used source localization analysis to determine the location and timing of activated brain areas involved in the generation of an event-related potential (ERP) component marker of executive attention in 10 adults with mTBI and in 10 matched healthy controls.