14 citations · 27 across the 11 of their papers we have counts for
11 papers
CAP: Towards PPG Universal Representation Learning with Patient-level Supervision
Chenyang He, Xinyi Shao, Shun Huang +4
Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. Yet existing approaches to universal PPG representation learning largel…
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals
Zhaoliang Chen, Cheng Ding, Saurabh Kataria +4
This study introduces a novel application of a Generative Pre-trained Transformer (GPT) model tailored for photoplethysmography (PPG) signals, serving as a foundation model for var…
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals
Cheng Ding, Zhicheng Guo, Zhaoliang Chen +3
Foundation models, especially those using transformers as backbones, have gained significant popularity, particularly in language and language-vision tasks. However, large foundati…
SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals
Runze Yan, Cheng Ding, Ran Xiao +4
Atrial fibrillation (AF), a common cardiac arrhythmia, significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography (PPG) offers a promising solut…
Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
Cheng Ding, Zhicheng Guo, Cynthia Rudin +3
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics. We state the comp…
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
Cheng Ding, Ran Xiao, Weijia Wang +2
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease,…
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
Zhicheng Guo, Cheng Ding, Duc H. Do +4
Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is associated with an increased risk of stroke, heart failure, and other cardiovascular complications, bu…
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables
Pranay Jain, Cheng Ding, Cynthia Rudin +1
Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG sig…
Sparse learned kernels for interpretable and efficient medical time series processing
Sully F. Chen, Zhicheng Guo, Cheng Ding +2
Rapid, reliable, and accurate interpretation of medical time-series signals is crucial for high-stakes clinical decision-making. Deep learning methods offered unprecedented perform…
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
Cheng Ding, Zhicheng Guo, Cynthia Rudin +7
Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmog…
Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss
Cheng Ding, Ran Xiao, Duc Do +4
Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascula…