Cay Gozde, Ravichandran Vignesh, Saikia Manob Jyoti, Hoffman Laurie, Laptook Abbot, Padbury James, Salisbury Amy L, Gitelson-Kahn Anna, Venkatasubramanian Krishna, Shahriari Yalda, Mankodiya Kunal
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI USA.
Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA USA.
J Signal Process Syst. 2022;94(6):543-557. doi: 10.1007/s11265-021-01669-9. Epub 2021 Jul 17.
The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing skin injuries, discomfort, and irritation. In addition, respiration rate (RR) monitoring in the NICU faces challenges of accuracy and clinical quality because RR is extracted from electrocardiogram (ECG). This research paper presents a design and validation of a smart textile pressure sensor system that addresses the existing challenges of medical monitoring in NICU. We designed two e-textile, piezoresistive pressure sensors made of Velostat for noninvasive RR monitoring; one was hand-stitched on a mattress topper material, and the other was embroidered on a denim fabric using an industrial embroidery machine. We developed a data acquisition system for validation experiments conducted on a high-fidelity, programmable NICU baby mannequin. We designed a signal processing pipeline to convert raw time-series signals into parameters including RR, rise and fall time, and comparison metrics. The results of the experiments showed that the relative accuracies of hand-stitched sensors were 98.68 (top sensor) and 98.07 (bottom sensor), while the accuracies of embroidered sensors were 99.37 (left sensor) and 99.39 (right sensor) for the 60 BrPM test case. The presented prototype system shows promising results and demands more research on textile design, human factors, and human experimentation.
全球早产婴儿数量不断增加,他们面临着严重的健康风险。这些婴儿需要在新生儿重症监护病房(NICU)接受持续护理。在NICU中,使用一组连接在身体上的有线粘性电极对早产儿的医学参数进行持续监测。电极上使用的医用粘合剂可能对婴儿有害,会导致皮肤损伤、不适和刺激。此外,NICU中的呼吸频率(RR)监测面临准确性和临床质量方面的挑战,因为RR是从心电图(ECG)中提取的。本文提出了一种智能纺织压力传感器系统的设计与验证,该系统解决了NICU中医学监测的现有挑战。我们设计了两个由维乐斯塔制成的电子纺织压阻式压力传感器,用于无创RR监测;一个手工缝制在床垫罩材料上,另一个使用工业绣花机绣在牛仔布上。我们开发了一个数据采集系统,用于在高保真、可编程的NICU婴儿模型上进行验证实验。我们设计了一个信号处理管道,将原始时间序列信号转换为包括RR、上升和下降时间以及比较指标在内的参数。实验结果表明,在60次每分钟呼吸次数(BrPM)的测试案例中,手工缝制传感器的相对准确率分别为顶部传感器98.68和底部传感器98.07,而绣花传感器的准确率分别为左侧传感器99.37和右侧传感器99.39。所展示的原型系统显示出了有前景的结果,并且需要在纺织品设计、人为因素和人体实验方面进行更多研究。