Commercialization of a Monitor to Assess Neurological Health in Neonates

描述（由申请人提供）：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）作为皮质功能的测量。然而，脑电图的使用已被确定为表征新生儿大脑皮层健康状况的唯一半客观手段之一。不幸的是，专家新生儿脑电图解释的可用性非常有限，并且在国内相对较少的专业儿科中心存在。即使可用，新生儿脑电图的审查和解释是时间和劳动密集型。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图监测，而不考虑其在神经学评估中的价值。该项目设想的设备是一种脑电图监测仪，可以监测新生儿的神经健康，具有完整的脑电图检查和临床医生验证的能力。为该监视器开发的算法专门分析新生儿脑电图明确定义的异常，以便在以下范围之一提供简单的神经评估评分（N3指数）：(i)正常，（ii）轻度异常，（iii）中度异常，或（iv）严重异常。评估算法包括信号处理技术，用于量化新生儿脑电图中出现的任何不连续性或不对称性的幅度、持续时间和频率组成。该算法还包括伪影效应最小化和前景事件与背景脑电图的区分方法。对选定的信号处理参数进行数据融合分析，计算出最终的评估分数，并随时间显示，从而产生可靠的脑电评估趋势。从所建议的监测中更准确地预测神经系统风险新生儿可能有几个好处。首先，它们可以影响护理决策的水平，特别是对于那些预后不良的新生儿（例如：严重的神经功能缺损或死亡）。其次，脑电图背景的持续评估可以作为治疗成功的衡量标准，表明治疗后神经系统的改善或下降。该项目的相关性在于，开发一种易于使用的新生儿脑电图监测仪，并采用准确的分析方法，可以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管理脑损伤，从而既可以改善结果，又可以降低医疗保健成本。

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.