Deep convolutional neural network-based signal quality assessment for photoplethysmogram

Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was to develop a signal quality assessment technology for photoplethysmogram (PPG) widely used in wearable healthcare. In this study, we developed and verified a deep neural network (DNN)-based signal quality assessment model using about 1.6 million 5-s segment length PPG big data of about 29 GB from the MIMIC III PPG waveform database. The DNN model was implemented through a 1D convolutional neural network (CNN). The number of CNN layers, number of fully connected nodes, dropout rate, batch size, and learning rate of the model were optimized through Bayesian optimization. As a result, 6 CNN layers, 1,546 fully connected layer nodes, 825 batch size, 0.2 dropout rate, and 0.002 learning rate were needed for an optimal model. Performance metrics of the result of classifying waveform quality into 'Good' and 'Bad', the accuracy, specificity, sensitivity, area under the receiver operating curve, and area under the precision-recall curve were 0.978, 0.948, 0.993, 0.985, 0.980, and 0.969, respectively. Additionally, in the case of simulated real-time application, it was confirmed that the proposed signal quality score tracked the decrease in pulse quality well.

Automated summary

A deep neural network-based signal quality assessment model for photoplethysmogram (PPG) was developed and optimized to achieve high accuracy in distinguishing waveform quality, with potential application in wearable healthcare.