论文标题
从心电图中提取分数灵感时间
Extracting Fractional Inspiratory Time from Electrocardiograms
论文作者
论文摘要
对肺和肺气道健康的非侵入性在家监测可以早期发现和跟踪哮喘和慢性阻塞性肺部疾病（COPD）等呼吸道疾病。各种提出的方法估计呼吸速率，并从心电图（ECG）信号中提取呼吸波形，以此作为谨慎监测肺部健康的一种方式。不幸的是，这些方法无法准确捕获呼吸周期阶段特征，从而导致非特异性，不完整的肺部健康情况。本文介绍了一种算法，通过将问题作为二进制分割任务框架来从ECG信号中提取更多呼吸信息。除了呼吸速率（RR）外，该算法还衍生了分数灵感时间（fit），这是对呼吸阶段信息导出的气道阻塞的直接衡量。该算法基于一个封闭式的复发性神经网络，该神经网络从单铅ECG信号中渗透了重要的呼吸信息。我们在模拟数据集和CEBS数据库中的5个主题上测量算法对5个主题的性能。我们的算法在估计呼吸速率方面保持出色的性能，并优于提取呼吸周期阶段和拟合/灵感的当前算法：呼气比率（IER）。 Our algorithm reports a root mean squared error (RMSE) of 0.06 in the computation of FIT (values range from 0.2-0.6) and a RMSE of 0.54 breaths per minute (bpm) for respiratory rate (values range from 8 - 28 breaths per minute (bpm)) on the MIMIC dataset, and an FIT RMSE of 0.11 and and RR RMSE of 0.66 bpm on the CEBS数据集。
Non-invasive at-home monitoring of lung and lung airways health enables the early detection and tracking of respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD). Various proposed approaches estimate the respiratory rate and extract the respiratory waveform from an electrocardiogram (ECG) signal as a way to discreetly monitor lung health. Unfortunately, these approaches fail to accurately capture the respiratory cycle phase features, resulting in a non-specific, incomplete picture of lung health. This paper introduces an algorithm to extract more respiratory information from the ECG signal by framing the problem as a binary segmentation task. In addition to respiratory rate (RR), the algorithm derives the fractional inspiratory time (FIT), a direct measure of airway obstruction derived from respiratory phase information. The algorithm is based on a gated recurrent neural network that infers vital respiratory information from a single-lead ECG signal. We measure our algorithm's performance on 5 subjects from the MIMIC dataset and 5 subjects from the CEBS database. Our algorithm maintains exceptional performance in estimating the respiratory rate and outperforms current algorithms that extract the respiratory cycle phases and FIT/ inspiratory:expiratory ratio (IER). Our algorithm reports a root mean squared error (RMSE) of 0.06 in the computation of FIT (values range from 0.2-0.6) and a RMSE of 0.54 breaths per minute (bpm) for respiratory rate (values range from 8 - 28 breaths per minute (bpm)) on the MIMIC dataset, and an FIT RMSE of 0.11 and and RR RMSE of 0.66 bpm on the CEBS dataset.
