Laguna Ana, Pusil Sandra, Acero-Pousa Irene, Zegarra-Valdivia Jonathan Adrián, Paltrinieri Anna Lucia, Bazán Àngel, Piras Paolo, Palomares I Perera Clàudia, Garcia-Algar Oscar, Orlandi Silvia
Zoundream AG, Basel, Switzerland.
Facultad de Medicina, Universidad Señor de Sipán, Chiclayo, Peru.
Front Neurosci. 2023 Sep 20;17:1266873. doi: 10.3389/fnins.2023.1266873. eCollection 2023.
Even though infant crying is a common phenomenon in humans' early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns.
Multimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants.
We found correlations between most of the features extracted from the signals depending on the infant's arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy.
Our findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.
尽管婴儿啼哭是人类早期生活中的常见现象，但研究人员要将其作为复杂神经生理功能的一种反映来正确理解，仍是一项挑战。我们的研究旨在根据新生儿不同的啼哭痛苦程度，确定新生儿哭声声学特征与神经生理信号及行为特征之间的关联。
收集了25名足月健康新生儿的多模态数据，同时记录婴儿哭声、脑电图（EEG）、近红外光谱（NIRS）以及面部表情和身体动作的视频。对该数据集进行统计分析，以确定三种不同婴儿状态（即休息、啼哭和痛苦）下变量之间的相关性。使用深度学习（DL）算法客观自动地评估婴儿的啼哭痛苦程度。
我们发现，根据婴儿的觉醒状态，从信号中提取的大多数特征之间存在相关性，其中包括：基频（F0）、脑活动（δ、θ和α频段）、大脑和身体的氧合作用、心率、面部张力和身体僵硬程度。此外，这些关联进一步证明，声学层面发生的情况可以通过行为和神经生理模式来表征。最后，开发的深度学习音频模型能够对不同程度的痛苦进行分类，准确率达到93%。
我们的研究结果强化了啼哭作为一种生物标志物的潜力，可以证明婴儿的身体、情感和健康状况，这对护理人员和临床医生来说是一个至关重要的工具。
