Device for continuous monitoring of cerebral activity in resuscitated patients.

Maynard, D.E.; Prior, Pamela F.; Scott, D. F.

doi:10.1136/bmj.4.5682.545-a

Cited by 240 publications

(106 citation statements)

References 3 publications

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“…Thickness of the tracing is proportional to the moment-to-moment amplitude variability, while the overall magnitude of the tracing reflects the voltage amplitude of the EEG at each period of time. For example, burst-suppression would result in a thick tracing due to alternating bursts and suppressions, whereas amplitude increases during seizure may result in increase in the overall magnitude of the tracing [111]. Electrodecremental seizures or decrease in EEG amplitudes during anesthesia may result in the opposite effect.…”

Section: Quantitative Tools and Technical Methodsmentioning

confidence: 99%

The Utility of EEG, SSEP, and Other Neurophysiologic Tools to Guide Neurocritical Care

Rosenthal

2012

Neurotherapeutics

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Neuromonitoring is an emerging field that aims to characterize real-time neurophysiology to tailor therapy for acute injuries of the central nervous system. While cardiac telemetry has been used for decades among patients requiring critical care of all kinds, neurophysiology and neurotelemetry has only recently emerged as a routine screening tool in comatose patients. The increasing utilization of electroencephalography in comatose patients is primarily due to the recognition of the common occurrence of nonconvulsive seizures among comatose patients, the development of quantitative measures to detect regional ischemia, and the appreciation of electroencephalography phenotypes that indicate prognosis after cardiac arrest. Other neuromonitoring tools, such as somatosensory evoked potentials have a complementary role, surveying the integrity of the neuroaxis as an indicator of prognosis or illness progression in both acute brain and spinal injuries.

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Section: Quantitative Tools and Technical Methodsmentioning

confidence: 99%

The Utility of EEG, SSEP, and Other Neurophysiologic Tools to Guide Neurocritical Care

Rosenthal

2012

Neurotherapeutics

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“…Data were filtered, rectified, and integrated over a window of 100 ms. To eliminate nonphysiological signal components and to slightly attenuate the dominant delta-wave patterns, a series of filters were applied to mimic those used in the Cerebral Function Monitor (24). The final signal was plotted logarithmically, and the resulting aEEG was analyzed by two different methods: a quantitative mean aEEG background technique, and a qualitative neural activity score technique.…”

Section: Methodsmentioning

confidence: 99%

Cerebral metabolic rate of oxygen and amplitude-integrated electroencephalography during early reperfusion after hypoxia-ischemia in piglets

Tichauer¹,

Elliott²,

Hadway³

et al. 2009

Journal of Applied Physiology

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Tichauer KM, Elliott JT, Hadway JA, Lee T, St. Lawrence KS. Cerebral metabolic rate of oxygen and amplitude-integrated electroencephalography during early reperfusion after hypoxia-ischemia in piglets. J Appl Physiol 106: 1506 -1512. First published March 19, 2009 doi:10.1152/japplphysiol.91156.2008.-The therapeutic window following perinatal hypoxia-ischemia is brief, and early clinical signs of injury can be subtle. Electroencephalography (EEG) represents the most promising early diagnostic of hypoxia-ischemia; however, some studies have questioned the sensitivity and specificity of EEG. The present study investigated the use of both near-infrared spectroscopy (NIRS) measurements of the cerebral metabolic rate of oxygen (CMRO 2) and amplitude-integrated EEG (aEEG) to detect the severity of hypoxia-ischemia after 1 h of reperfusion in newborn piglets (10 insult, 3 control). The CMRO2 was measured before and after 1 h of reperfusion from hypoxia-ischemia, the duration of which was varied from piglet to piglet with a range of 3-24 min, under fentanyl/nitrous oxide anesthesia to mimic awake-like levels of cerebral metabolism. EEG data were collected throughout the study. On average, the CMRO2 and mean aEEG background signals were significantly depressed following the insult (P Ͻ 0.05). Mean CMRO 2 and mean aEEG background were 2.61 Ϯ 0.11 ml O2 ⅐ min Ϫ1 ⅐ 100 g Ϫ1 and 20.4 Ϯ 2.7 V before the insult and 1.58 Ϯ 0.09 ml O2⅐min Ϫ1 ⅐100 g Ϫ1 and 11.8 Ϯ 2.9 V after 1 h of reperfusion, respectively. Both CMRO2 and aEEG displayed statistically significant correlations with duration of ischemia (P Ͻ 0.05; r ϭ 0.71 and r ϭ 0.89, respectively); however, only CMRO2 was sensitive to milder injuries (Ͻ5 min). This study highlights the potential for combining NIRS measures of CMRO2 with EEG in the neonatal intensive care unit to improve early detection of perinatal hypoxia-ischemia. near-infrared spectroscopy; piglet; hypoxia-ischemia; electroencephalography PERINATAL HYPOXIC-ISCHEMIC encephalopathy is a substantial cause of infant mortality and morbidity. It remains a detrimental factor in roughly 1 of every 500 live term births in developed nations, leading to long-term neurological and developmental disabilities (20). Recent clinical trials have demonstrated that the incidence of death and disability from hypoxia-ischemia in newborns can be significantly reduced by initiating treatment strategies, hypothermia, for example, after birth and within a 6-h therapeutic window (9,11,14,32). Due to the brevity of the therapeutic window, early detection of injury and early determination of those infants who are likely candidates for treatment are crucial (15). In this regard, traditional early indicators of brain injury, including Apgar scores, umbilical artery acidosis, and fetal heart rate monitoring, suffer from poor specificity (7, 31), and more specific indicators of injury, such as magnetic resonance imaging and spectroscopy, are insensitive or difficult to implement within the therapeutic window (6, 21).To this point, the most...

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“…Unlike in [21], the Amplitude Integrated EEG (aEEG) is widely used to detect neonatal seizures, despite the fact that the accuracy of seizure recognition can be moderate, especially in brief, low amplitude, focally oriented seizures [22]. Neonatologists analyze the aEEG signals at the patient's bedside.…”

Section: Related Work 31 Statistical Detectionmentioning

confidence: 99%

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

Zaghloul¹,

Elsayed²,

Bayoumi³

2017

IJCA

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Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study a system of a wireless wearable adaptive for early prediction of epilepsy seizures is proposed, works via minimally invasive wireless technology paired with an external control device (e.g., a doctors' smartphone), with a higher than standard accuracy (71%) and prediction time (14.56 sec). This novel architecture has not only opened new opportunities for daily usable BCI implementations, but they can also save a life by helping to prevent a seizure's fatal consequences. General TermsBrain Machine Interface, Artificial Immune, Signal Processing.

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Device for continuous monitoring of cerebral activity in resuscitated patients.

Cited by 240 publications

References 3 publications

The Utility of EEG, SSEP, and Other Neurophysiologic Tools to Guide Neurocritical Care

The Utility of EEG, SSEP, and Other Neurophysiologic Tools to Guide Neurocritical Care

Cerebral metabolic rate of oxygen and amplitude-integrated electroencephalography during early reperfusion after hypoxia-ischemia in piglets

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

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