ArticlePhysiological measurement2019
Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.
Article in Physiological measurement, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 54 citations in OpenAlex.
- Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings.IEEE transactions on bio-medical engineering · 2026Article
- Sparse learned kernels for interpretable and efficient medical time series processing.Nature machine intelligence · 2024Article
- Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet.Sensors (Basel, Switzerland) · 2024Article
- Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation.Sensors (Basel, Switzerland) · 2024Article
- Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms.IEEE journal of biomedical and health informatics · 2024Article
- The 2023 wearable photoplethysmography roadmap.Physiological measurement · 2023Review
- [A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Log-Spectral Matching GAN: PPG-Based Atrial Fibrillation Detection can be Enhanced by GAN-Based Data Augmentation With Integration of Spectral Loss.IEEE journal of biomedical and health informatics · 2023Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal.Sensors (Basel, Switzerland) · 2022Article
- Robust PPG Peak Detection Using Dilated Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches.Scientific reports · 2022Article
- Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.JMIR medical informatics · 2022Review
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
- Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment.IEEE access : practical innovations, open solutions · 2021Article
- Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.Respiratory physiology & neurobiology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectivePhotoplethysmography (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
resultsResNet18 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.