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首页> 外文会议>Computing in Cardiology >Stationary Wavelet Transform for the Extraction of the Impedance Circulation Component During Out-of-hospital Cardiac Arrest
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Stationary Wavelet Transform for the Extraction of the Impedance Circulation Component During Out-of-hospital Cardiac Arrest

机译：静止小波变换，用于在医院外卡骤停期间提取阻抗循环分量

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An automated pulse detector during out-of-hospital cardiac arrest (OHCA) is needed. The thoracic impedance (TI) recorded through defibrillation pads presents an impedance circulation component (ICC), hidden among other components, in the form of small fluctuations correlated with each effective heartbeat. This study presentes a method based on the stationary wavelet transform (SWT) to derive the ICC. A dataset with 456 5-s segments, 175 pulseless electrical activity (PEA) and 281 pulse-generating rhythm (PR), with concurrent ECG and TI signals from 49 OHCA patients was used. The SWT was used to decompose the TI into 7 levels. The ICC was derived from soft denoised d6-d7 or d7 detail coefficients for segments with heart rate ≥93 bpm and <; 93 bpm, respectively. Six features characterizing the amplitude and area of the ICC and its first derivative (dICC) were calculated. Their PEA/PR discrimination power was measured using the area under the curve (AUC). These AUCs were compared with those obtained for the same features derived from the ICC/dICC extracted using an adaptive recursive least-squares (RLS) algorithm. The six features showed a mean (standard deviation) AUC of 0.91 (0.03) while RLS-based features yielded an AUC of 0.85 (0.07). Combining these ICC/dICC features with ECG features in a machine learning classifier might result in a robust pulse detector.
机译：需要在医院外心脏骤停（OHCA）期间的自动脉冲检测器。通过除颤焊盘记录的胸阻抗（TI）呈现阻抗循环分量（ICC），其隐藏在其他部件中，以与每个有效心跳相关的小波动的形式。本研究提出了一种基于静止小波变换（SWT）来导出ICC的方法。使用具有456个5-S段的数据集，使用来自49个OHCA患者的并发的ECG和TI信号，175毫瓦电活动（PEA）和281脉冲节律（PR）。 SWT用于将TI分解成7个级别。 ICC源自软去噪度 6 -D. 7 或D. 7 具有心率≥93bpm的细分的细节系数≥93bpm和<; 93 BPM分别。计算了表征ICC的幅度和区域及其第一衍生物（DICC）的六个特征。使用曲线下（AUC）下的区域测量其豌豆/公关歧视功率。将这些AUC与使用自适应递归最小二乘（RLS）算法提取的ICC / DICC的相同特征进行比较。六种特征显示为0.91（0.03）的平均值（标准偏差）AUC，而基于RLS的特征产生0.85的AUC（0.07）。将这些ICC / DICC功能与机器学习分类器中的ECG功能相结合可能导致稳健的脉冲检测器。

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