Department of Biomedical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu 59626, Korea.
Sensors (Basel). 2021 Mar 20;21(6):2188. doi: 10.3390/s21062188.
The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'poor' classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a 'poor' class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.
本研究的目的是开发一种机器学习模型，该模型无需复杂的预处理，即可根据光体积描记图的形状和脉动波形中的相位相关性，准确评估光体积描记图的质量。对 76 名参与者（每名参与者 5 分钟）进行光体积描记记录。对所有记录的光体积描记图进行分段，以获得总共 49561 个脉动段。这些脉动段被手动标记为“良好”和“不良”类别，并使用递归图转换为二维相位空间轨迹图像。分类模型使用具有两层结构的卷积神经网络实现。结果，所提出的模型正确分类了 49561 个脉动段中的 48827 个脉动段，错误分类了 734 个脉动段，平衡准确率为 0.975。对于“不良”类别的测试数据集，所开发模型的灵敏度、特异性和阳性预测值分别为 0.964、0.987 和 0.848，曲线下面积为 0.994。本研究中提出的使用递归图作为输入的卷积神经网络模型可以作为一种具有高精度的通用模型，通过数据扩展来进行信号质量评估。它具有无需复杂预处理或特征检测过程的优点。
