Update on Minimal Standards for Electroencephalography in Canada: A Review by the Canadian Society of Clinical Neurophysiologists

Dash, Dianne; Dash, Chelsea; Primrose, Sara; Hernández-Ronquillo, Lizbeth; Moien‐Afshari, Farzad; Ladino, Lady Diana; Appendino, Juan Pablo; Mazepa, Lindsay; Elliott, Christine; Mirsattari, Seyed M.; Federico, Paolo; Bui, Esther; Hunter, Gary; RamachandranNair, Rajesh; Sharma, Rohit; Melendres, Paula; Nikkel, Joanne; Nguyen, Dang Khoa; Almubarak, Salah; Rigby, Mike; Téllez‐Zenteno, José F.

doi:10.1017/cjn.2017.217

Cited by 25 publications

(20 citation statements)

References 61 publications

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“…Scalp EEG technology remains the clinical gold standard for the noninvasive assessment of electrical brain activity (Dash et al, 2017). Using EEG signals in conjunction with predicted brain hemodynamics can possibly improve clinical management and ultimately patient outcomes (Connolly et al, 2015;Helbok & Claassen, 2013).…”

Section: Discussionmentioning

confidence: 99%

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

et al. 2021

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In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model’s fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

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Section: Discussionmentioning

confidence: 99%

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

et al. 2021

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“…Regarding hyperventilation, the patient will be asked to breathe deeply for 3 min. These procedures are aligned with the minimal standards for EEG in Canada (16). During both routine EEGs, the technologist annotates any events.…”

Section: Electroencephalogramsmentioning

confidence: 99%

Diagnostic Accuracy of the Ambulatory EEG vs. Routine EEG for First Single Unprovoked Seizures and Seizure Recurrence: The DX-Seizure Study

et al. 2020

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Background: The DX-Seizure study aims to evaluate the diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratio) of the ambulatory EEG in comparison with the first routine EEG, and a second routine EEG right before the ambulatory EEG, on adult patients with first single unprovoked seizure (FSUS) and define the utility of ambulatory EEG in forecasting seizure recurrence in these patients after 1-year follow-up. Methods: The DX-Seizure study is a prospective cohort of 113 adult patients (≥18-yearold) presenting with FSUS to the Single Seizure Clinic for evaluation. These patients will be assessed by a neurologist/epileptologist with the first routine EEG (referral EEG) and undergo a second routine EEG and ambulatory EEG. The three EEG (first routine EEG as gold standard) will be compared and evaluated their diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios) with respect of epileptiform activity and other abnormalities. One-year follow-up of each patient will be used to assess recurrence of seizures after a FSUS and the utility of the ambulatory EEG to forecast these recurrences. Discussion: To the best of our knowledge, this will be the first study to prospectively examine the use of ambulatory EEG for a FSUS in adults and its use for prediction of recurrence of seizures. The overarching goal is to improve diagnostic accuracy with the use of ambulatory EEG in patients with their FSUS. We anticipate that this will decrease incorrect or uncertain diagnoses with resulting psychological and financial cost to the patient. We also anticipate that an improved method to predicting the recurrence of seizures will reduce the chances of repeated seizures and their consequences.

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“…The design, specification, maintenance and calibration protocols for electrodes [99][100][101][102][103][104][105][106][107][108][109], electrode placement systems [109][110][111][112][113][114][115][116][117][118], use of ground and reference electrodes, EEG machines (analog, digital or multi-channel), EEG calibrators and montages [119] are governed by stringent standards and guidelines [120][121][122][123][124][125][126][127][128][129]. EEG recording procedures are categorized into two stages: a) data acquisition and pre-processing, and b) feature extraction.…”

Section: Current Eeg Technology In a Nutshellmentioning

confidence: 99%

“…The cerebral cortex is divided into four lobes: the frontal, parietal, temporal and occipital; each of which performs specific functions. The 4mm thick cerebral cortex was mapped out by when the analog signal is digitized by the analog-to-digital converter [122][123][124][125][126][127][128].…”

Section: Introduction Eeg In Brain Health and Cognitionmentioning

confidence: 99%

Overview of current diagnostic, prognostic, and therapeutic use of EEG and EEG-based markers of cognition, mental, and brain health

Miranda¹,

Cox²,

Alexander³

et al. 2019

Integr Mol Med

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The global burden of cognitive, mental, neurological, and substance-use disorders at 258 million disability adjusted life years calls for immediate "action" in their prevention and management. The electroencephalogram (EEG) is one of the most widely-used instruments for the non-invasive neuro-physiological measure of brain function and health. The EEG was originally used to solely monitor and record electric waves generated by electrical activity in the brain to aid in clinical decision-making and diagnosis. Technological improvements have made it possible for state-of the-art EEG computer-based systems like NeuralScan by Medeia Inc. to evaluate changes in power and in ratios of these brain waves with changes in brain and mental health status. Today's EEG machines can also identifying the precise localization of these changes enabling more accurate diagnosis and treatment. Improvements in EEG technology have made them robust, stationary/portable, high fidelity, versatile with the ability to carry out complex functions and calculations yet still be user/clinician-friendly highlighting their potential for use in clinical, research, epidemiological and public health settings. The present article presents an overview on EEG machines, their use in diagnosis, prognosis and therapy and to generate EEG-based markers in the area of cognition, mental and brain health.

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Update on Minimal Standards for Electroencephalography in Canada: A Review by the Canadian Society of Clinical Neurophysiologists

Cited by 25 publications

References 61 publications

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

Diagnostic Accuracy of the Ambulatory EEG vs. Routine EEG for First Single Unprovoked Seizures and Seizure Recurrence: The DX-Seizure Study

Overview of current diagnostic, prognostic, and therapeutic use of EEG and EEG-based markers of cognition, mental, and brain health

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