Journal Article

Deep convolutional neural network-based signal quality assessment for photoplethysmogram

Shin, Hangsik

2022

Overview

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. •Signal quality assessment using raw photoplethysmogram without pre-processing.•Deep learning-based photoplethysmogram quality assessment.•Validation using 30 times or more of big data compared to existing studies.•Secured high performance (0.98 of area under curve) with high reliability.

• Publisher: Elsevier Ltd,Elsevier Limited
• Subject: Accuracy / Artificial neural networks / Bayes Theorem / Bayesian analysis / Big Data / Blood pressure / Convolutional neural network / Deep learning / Health care / Heart Rate / Internal Medicine / Machine learning / Medical equipment / Motion artifacts / Neural networks / Neural Networks, Computer / Nodes / Optimization / Other / Performance measurement / Photoplethysmogram / Photoplethysmography / Pulse quality assessment / Quality assessment / Quality control / Signal quality / Signal quality assessment / Technology assessment / Waveforms / Wearable computers / Wearable technology