Nasal inhalation is the basis of olfactory perception and drives neural activity in olfactory and limbic brain regions. Therefore, our ability to investigate the neural underpinnings of olfaction and respiration can only be as good as our ability to characterize features of respiratory behavior. However, recordings of natural breathing are inherently nonstationary, nonsinusoidal, and idiosyncratic making feature extraction difficult to automate. The absence of a freely available computational tool for characterizing respiratory behavior is a hindrance to many facets of olfactory and respiratory neuroscience. To solve this problem, we developed BreathMetrics, an open-source tool that automatically extracts the full set of features embedded in human nasal airflow recordings. Here, we rigorously validate BreathMetrics' feature estimation accuracy on multiple nasal airflow datasets, intracranial electrophysiological recordings of human olfactory cortex, and computational simulations of breathing signals. We hope this tool will allow researchers to ask new questions about how respiration relates to body, brain, and behavior.

中文翻译：

---

使用 BreathMetrics 自动分析呼吸波形：呼吸信号处理工具箱。


鼻吸气是嗅觉感知的基础，驱动嗅觉和边缘脑区域的神经活动。因此，我们研究嗅觉和呼吸的神经基础的能力只能与我们表征呼吸行为特征的能力一样好。然而，自然呼吸的记录本质上是不稳定的、非正弦的和特殊的，使得特征提取难以自动化。缺乏用于表征呼吸行为的免费计算工具是嗅觉和呼吸神经科学许多方面的障碍。为了解决这个问题，我们开发了 BreathMetrics，这是一种开源工具，可以自动提取人类鼻腔气流记录中嵌入的全套特征。在这里，我们严格验证 BreathMetrics 在多个鼻气流数据集、人类嗅觉皮层的颅内电生理记录以及呼吸信号的计算模拟上的特征估计准确性。我们希望这个工具能让研究人员提出有关呼吸与身体、大脑和行为之间关系的新问题。