Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Journal Article (Journal Article)

Objective

Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as continuous long-term monitoring of heart arrhythmias, fitness, and sleep tracking, and hydration monitoring. One major issue that can hinder PPG-based applications is movement artifacts, which can lead to false interpretations. In many implementations, noisy PPG signals are discarded. Misinterpreted or discarded PPG signals pose a problem in applications where the goal is to increase the yield of detecting physiological events, such as in the case of paroxysmal atrial fibrillation (AF)-a common episodic heart arrhythmia and a leading risk factor for stroke. In this work, we compared a traditional machine learning and deep learning approaches for PPG quality assessment in the presence of AF, in order to find the most robust method for PPG quality assessment.

Approach

The training data set was composed of 78 278 30 s long PPG recordings from 3764 patients using bedside patient monitors. Two different representations of PPG signals were employed-a time-series based (1D) one and an image-based (2D) one. Trained models were tested on an independent set of 2683 30 s PPG signals from 13 stroke patients.

Main results

ResNet18 showed a higher performance (0.985 accuracy, 0.979 specificity, and 0.988 sensitivity) than SVM and other deep learning approaches. 2D-based models were generally more accurate than 1D-based models.

Significance

2D representation of PPG signal enhances the accuracy of PPG signal quality assessment.

Full Text

Duke Authors

Cited Authors

  • Pereira, T; Ding, C; Gadhoumi, K; Tran, N; Colorado, RA; Meisel, K; Hu, X

Published Date

  • December 27, 2019

Published In

Volume / Issue

  • 40 / 12

Start / End Page

  • 125002 -

PubMed ID

  • 31766037

Pubmed Central ID

  • PMC7198064

Electronic International Standard Serial Number (EISSN)

  • 1361-6579

International Standard Serial Number (ISSN)

  • 0967-3334

Digital Object Identifier (DOI)

  • 10.1088/1361-6579/ab5b84

Language

  • eng