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经皮胆红素连续监测的信号质量。

Signal Quality in Continuous Transcutaneous Bilirubinometry.

机构信息

Department of Physics, Faculty of Science and Technology, NOVA University of Lisbon, Caparica Campus, 2829-516 Caparica, Portugal.

Department of Pediatrics, Hospital Garcia de Orta, EPE, 2805-267 Almada, Portugal.

出版信息

Sensors (Basel). 2024 Sep 23;24(18):6154. doi: 10.3390/s24186154.

Abstract

Bilirubin is a product of the metabolism of hemoglobin from red blood cells. Higher levels of bilirubin are a sign that either there is an unusual breaking down rate of red blood cells or the liver is not able to eliminate bilirubin, through bile, into the gastrointestinal tract. For adults, bilirubin is occasionally monitored through urine or invasive blood sampling, whilst all newborns are routinely monitored visually, or non-invasively with transcutaneous measurements (TcBs), due to their biological immaturity to conjugate bilirubin. Neonatal jaundice is a common condition, with higher levels of unconjugated bilirubin concentration having neurotoxic effects. Actual devices used in TcBs are focused on newborn populations, are hand-held, and, in some cases, operate in only two wavelengths, which does not necessarily guarantee reliable results over all skin tones. The same occurs with visual inspections. Based on that, a continuous bilirubin monitoring device for newborns is being developed to overcome visual inspection errors and to reduce invasive procedures. This device, operating optically with a mini-spectrometer in the visible range, is susceptible to patient movements and, consequently, to situations with a lower signal quality for reliable bilirubin concentration estimates on different types of skin. Therefore, as an intermediate development step and, based on skin spectra measurements from adults, this work addresses the device's placement status prediction as a signal quality indication index. This was implemented by using machine learning (ML), with the best performances being achieved by support vector machine (SVM) models, based on the spectra acquired on the arm and forehead areas.

摘要

胆红素是红细胞中血红蛋白代谢的产物。胆红素水平升高表明红细胞的分解速度异常,或者肝脏无法通过胆汁将胆红素排入胃肠道。对于成年人,胆红素偶尔通过尿液或有创性血液采样进行监测,而所有新生儿都通过视觉或非侵入性经皮测量(TcBs)进行常规监测,这是由于他们的胆红素结合生物不成熟。新生儿黄疸是一种常见病症,未结合胆红素浓度升高具有神经毒性作用。TcBs 中实际使用的设备专注于新生儿人群,是手持式的,在某些情况下,仅使用两个波长运行,这并不一定能保证在所有肤色下都能获得可靠的结果。视觉检查也是如此。基于此,正在开发一种用于新生儿的连续胆红素监测设备,以克服视觉检查误差并减少有创程序。该设备通过在可见范围内使用微型光谱仪进行光学操作,容易受到患者运动的影响,因此,在不同类型的皮肤下,信号质量较低的情况下,可靠地估计胆红素浓度的情况也会受到影响。因此,作为中间开发步骤,并基于成人的皮肤光谱测量,本工作将设备的放置状态预测作为信号质量指示指数。这是通过使用机器学习(ML)来实现的,基于在手臂和额头区域采集的光谱,支持向量机(SVM)模型取得了最佳性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ca5/11435595/a5ead49399c8/sensors-24-06154-g001.jpg

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