ArticleJournal of the American Heart Association2022
Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network.
Article in Journal of the American Heart Association, 2022. 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, 44 citations in OpenAlex.
- A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure.BioData mining · 2026Article
- A Multimodal Closed-Loop Framework for Vital Sign Monitoring and Intelligent Diagnosis of Amusement Ride Passengers Under High-Dynamic Motion.Sensors (Basel, Switzerland) · 2026Article
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- Multi-Class Arrhythmia Detection from PPG Signals Based on VGG-BiLSTM Hybrid Deep Learning Model.Biosensors · 2026Article
- Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings.IEEE transactions on bio-medical engineering · 2026Article
- Multi class photoplethysmography-based deep model for cardiovascular disease classification.PloS one · 2026Article
- Development of an efficient deep learning model for arrhythmia detection from consumer smartwatches.Frontiers in artificial intelligence · 2026Article
- Multi-Classification Model for PPG Signal Arrhythmia Based on Time-Frequency Dual-Domain Attention Fusion.Sensors (Basel, Switzerland) · 2025Article
- A Dual-Modal Wearable Pulse Detection System Integrated with Deep Learning for High-Accuracy and Low-Power Sleep Apnea Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals.Journal of medical systems · 2025Article
- FMCW-based contactless heart rate monitoring.Scientific reports · 2025Article
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Advancing cardiac diagnostics: high-accuracy arrhythmia classification with the EGOLF-net model.Frontiers in physiology · 2025Article
- Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.Diagnostics (Basel, Switzerland) · 2024Article
- Photoplethysmography based atrial fibrillation detection: a continually growing field.Physiological measurement · 2024Review
- Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been reported. Our study investigated the feasibility of using photoplethysmography signals and a deep convolutional neural network to classify multiclass arrhythmia types. Methods and Results ECG and photoplethysmography signals were collected simultaneously from a group of patients who underwent radiofrequency ablation for arrhythmias. A deep convolutional neural network was developed to classify multiple rhythms based on 10-second photoplethysmography waveforms. Classification performance was evaluated by calculating the area under the microaverage receiver operating characteristic curve, overall accuracy, sensitivity, specificity, and positive and negative predictive values against annotations on the rhythm of arrhythmias provided by 2 cardiologists consulting the ECG results. A total of 228 patients were included; 118 217 pairs of 10-second photoplethysmography and ECG waveforms were used. When validated against an independent test data set (23 384 photoplethysmography waveforms from 45 patients), the DCNN achieved an overall accuracy of 85.0% for 6 rhythm types (sinus rhythm, premature ventricular contraction, premature atrial contraction, ventricular tachycardia, supraventricular tachycardia, and atrial fibrillation); the microaverage area under the microaverage receiver operating characteristic curve was 0.978; the average sensitivity, specificity, and positive and negative predictive values were 75.8%, 96.9%, 75.2%, and 97.0%, respectively. Conclusions This study demonstrated the feasibility of classifying multiclass arrhythmias from photoplethysmography signals using deep learning techniques. The approach is attractive for population-based screening and may hold promise for the long-term surveillance and management of arrhythmia. Registration URL: www.chictr.org.cn. Identifier: ChiCTR2000031170.
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