论文标题
数字听诊器记录的胸部声音，出生时婴儿的新生儿呼吸窘迫的预测
Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds
论文作者
论文摘要
新生儿呼吸窘迫是一种常见的疾病，如果不治疗，可能会导致短期和长期并发症。本文调查了在1分钟后传递后记录的数字听觉听觉记录的胸部声音，以实现新生儿呼吸窘迫的早期检测和预测。这项研究包括了51个新生儿，其中9个呼吸窘迫。对于每个新生儿，拍摄了1分钟的前和后记录。这些记录是预处理的，以消除嘈杂的片段并获得高质量的心脏和肺部声音。然后，对随机的不足采样提升（Rusboost）分类器进行了各种功能的培训，例如功率和生命体征功能，从心脏和肺部声音中提取。 Rusboost算法产生的特异性，灵敏度和准确性结果分别为85.0％，66.7％和81.8％。
Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.
