Urdal Jarle, Engan Kjersti, Eftestøl Trygve, Naranjo Valery, Haug Ingunn Anda, Yeconia Anita, Kidanto Hussein, Ersdal Hege
Department of Electrical Engineering and Computer Science, University of Stavanger, Norway.
Department of Electrical Engineering and Computer Science, University of Stavanger, Norway.
Comput Methods Programs Biomed. 2020 Sep;193:105445. doi: 10.1016/j.cmpb.2020.105445. Epub 2020 Mar 14.
Early neonatal death is a worldwide challenge with 1 million newborn deaths every year. The primary cause of these deaths are complications during labour and birth asphyxia. The majority of these newborns could have been saved with adequate resuscitation at birth. Newborn resuscitation guidelines recommend immediate drying, stimulation, suctioning if indicated, and ventilation of non-breathing newborns. A system that will automatically detect and extract time periods where different resuscitation activities are performed, would be highly beneficial to evaluate what resuscitation activities that are improving the state of the newborn, and if current guidelines are good and if they are followed. The potential effects of especially stimulation are not very well documented as it has been difficult to investigate through observations. In this paper the main objective is to identify stimulation activities, regardless if the state of the newborn is changed or not, and produce timelines of the resuscitation episode with the identified stimulations.
Data is collected by utilizing a new heart rate device, NeoBeat, with dry-electrode ECG and accelerometer sensors placed on the abdomen of the newborn. We propose a method, NBstim, based on time domain and frequency domain features from the accelerometer signals and ECG signals from NeoBeat, to detect time periods of stimulation. NBstim use causal features from a gliding window of the signals, thus it can potentially be used in future realtime systems. A high performing feature subset is found using feature selection. System performance is computed using a leave-one-out cross-validation and compared with manual annotations.
The system achieves an overall accuracy of 90.3% when identifying regions with stimulation activities.
The performance indicates that the proposed NBstim, used with signals from the NeoBeat can be used to determine when stimulation is performed. The provided activity timelines, in combination with the status of the newborn, for example the heart rate, at different time points, can be studied further to investigate both the time spent and the effect of different newborn resuscitation parameters.
新生儿早期死亡是一项全球性挑战，每年有100万新生儿死亡。这些死亡的主要原因是分娩期间的并发症和出生窒息。这些新生儿中的大多数若在出生时得到充分复苏本可挽救。新生儿复苏指南建议立即擦干、刺激、必要时吸痰以及对无呼吸的新生儿进行通气。一个能够自动检测并提取进行不同复苏活动时间段的系统，对于评估哪些复苏活动正在改善新生儿状况、当前指南是否完善以及是否得到遵循将非常有益。特别是刺激的潜在效果尚未得到很好的记录，因为通过观察进行研究很困难。本文的主要目的是识别刺激活动，无论新生儿的状态是否改变，并生成包含已识别刺激的复苏过程时间线。
通过使用一种新的心率设备NeoBeat收集数据，该设备带有置于新生儿腹部的干电极心电图和加速度计传感器。我们基于来自NeoBeat的加速度计信号和心电图信号的时域和频域特征，提出一种名为NBstim的方法来检测刺激时间段。NBstim使用信号滑动窗口的因果特征，因此它有可能用于未来的实时系统。通过特征选择找到一个高性能的特征子集。使用留一法交叉验证计算系统性能，并与人工标注进行比较。
该系统在识别有刺激活动的区域时总体准确率达到90.3%。
该性能表明，所提出的NBstim与来自NeoBeat的信号一起使用时，可用于确定何时进行刺激。所提供的活动时间线，结合新生儿在不同时间点的状态，例如心率，可以进一步研究以调查不同新生儿复苏参数所花费的时间和效果。
