Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
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DOI：
10.1088/0967-3334/33/9/1491
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发表时间：
2012-09-01
影响因子：
3.2
通讯作者：
Clifford, G. D.
中科院分区：
文献类型：
--
作者：
Li, Q.;Clifford, G. D.
在这项工作中，我们描述了一种用于评估搏动波形临床效用的逐拍方法，主要记录心血管血容量或压力变化，重点是光容积脉搏图（PPG）。生理血流是非平稳的，脉搏的高度、宽度和形态会随着心率、心输出量、传感器类型和硬件或软件预处理要求的变化而变化。此外，存在相当大的个体间和传感器位置变化。因此，简单的模板匹配方法是不合适的，因此需要针对特定患者的自适应初始化。我们引入动态时间扭曲来拉伸每个节拍以匹配运行模板，并将其与与信号质量相关的其他几个特征相结合，包括相关性和似乎被剪切的节拍的百分比。然后将特征提交给多层感知器神经网络，以学习存在优质和劣质脉冲时参数之间的关系。一个专家标记的数据库包含1055段PPG，每段6 s长，记录了104个独立的危重病患者在正常和证实的心律失常事件期间的记录，用于训练和测试我们的算法。训练集的准确率为97.5%，测试集的准确率为95.2%。该算法可作为独立的信号质量评估算法，用于审查PPG痕迹或任何类似的准周期信号的临床效用。
In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.