Commercialization of a Monitor to Assess Neurological Health in Neonates

描述(申请人提供)：与成人不同，新生儿不能接受传统的精神状态测试(定向、语言、记忆等)。作为皮质功能的一种衡量标准。然而，脑电的使用被公认为唯一的半客观手段之一，以表征新生儿大脑皮层的健康状况。不幸的是，专家对新生儿脑电的解释非常有限，而且目前国内的儿科专科中心相对较少。即使有机会，对新生儿脑电的审查和解释也是时间和劳动密集型的。由于这些限制因素，许多医院很少在新生儿重症监护病房进行脑电监测，无论其在神经学评估中的价值如何。该项目设想的设备是一种脑电监测仪，可以跟踪新生儿的神经健康状况，具有由临床医生完成脑电检查和验证的能力。为该监护仪开发的算法专门分析新生儿EEG中明确定义的异常，以便提供以下范围之一的简单神经评估评分(N3指数)：(I)正常、(Ii)轻度异常、(Iii)中度异常或(Iv)严重异常。评估算法由信号处理技术组成，用于量化新生儿脑电中出现的任何不连续或不对称的幅度、持续时间和频率组成。该算法还包括伪影效果最小化和从背景脑电信号中区分前景事件的方法。对所选信号处理参数的数据融合分析计算最终评估分数，该最终评估分数随时间显示以产生可靠的EEG评估趋势。通过拟议的监护仪对神经学高危新生儿进行更准确的预测可能有几个好处。首先，它们可能影响护理水平的决定，特别是对于那些预后不佳的新生儿(例如：严重的神经缺陷或死亡)。其次，对脑电背景的持续评估可以作为治疗成功的衡量标准，表明治疗后神经功能改善或下降。拟议项目的相关性在于，开发一种易于使用的具有准确分析方法的新生儿脑电监测仪，可以使更广泛地监测新生儿的大脑状态。它还将使这一人群能够更早地发现和处理脑损伤，这可以促进改善结果和降低医疗保健成本。

DESCRIPTION (provided by applicant): Unlike adults, neonates cannot undergo traditional mental status testing (orientation, language, memory, etc.) as a measure of cortical function. However, the use of EEG is well established as one of the only semi-objective means to characterize the health of the cerebral cortex in neonates. Unfortunately, the availability of expert neonatal EEG interpretation is very limited, and is present at relatively few specialized pediatric centers in the country. Even when available, the review and interpretation of neonatal EEG is time and labor intensive. Because of these limiting factors, many hospitals rarely perform EEG monitoring in the neonatal intensive care unit, regardless of its value in neurological assessment. The envisioned device from this project is an EEG monitor that can trend the neurological wellness of neonates, with the capability for complete EEG review and verification by clinicians. The algorithm being developed for this monitor specifically analyzes neonatal EEG for clearly defined abnormalities in order to provide a simple neurological assessment score (the N3 Index) in one of the following ranges: (i) normal, (ii) mildly abnormal, (iii) moderately abnormal, or (iv) severely abnormal. The assessment algorithm consists of signal processing techniques used to quantify the amplitudes, durations, and frequency compositions of any discontinuities or asymmetries present in neonatal EEGs. The algorithm also includes methods for artifact effect minimization and the discrimination of foreground events from the background EEG. A data fusion analysis of the selected signal processing parameters calculates the final assessment score, which is displayed over time to produce a reliable trend of EEG assessment. More accurate prognoses of neurologically at-risk newborns from the proposed monitor may have several benefits. First, they can impact level of care decisions, especially for those neonates given poor prognoses (e.g.: severe neurological deficits or death). Secondly, the continuous assessment of the EEG background can be a measure of treatment success, indicating neurological improvement or decline after treatment has been given. The relevance of the proposed project is that the development of an easy-to-use neonatal EEG monitor with accurate analysis methods could enable more widespread monitoring of brain status in newborns. It also would enable earlier detection and management of brain injury in this population, which could promote both improved outcomes and reduced health care costs.