- 2013 - Time-varying coherence function for atrial fibrillation detection - [Paper]
- 2016 - Atrial fibrillation detection using photo-plethysmography and acceleration data at the wrist - [Paper]
- 2016 - Monitoring and detecting atrial fibrillation using wearable technology - [Paper]
- 2016 - PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application - [Paper]
- 2016 - Smart detection of atrial fibrillation - [Paper]
- 2017 - A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology - [Paper]
- 2017 - Detection of atrial fibrillation using an earlobe photoplethysmographic sensor - [Paper]
- 2017 - Identification of atrial fibrillation by quantitative analyses of fingertip photoplethysmogram - [Paper]
- 2018 - Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions - [Paper]
- 2018 - Developing a novel noise artifact detection algorithm for smartphone PPG signals: preliminary results - [Paper]
- 2018 - Motion and noise artifact-resilient atrial fibrillation detection using a smartphone - [Paper]
- 2018 - The accuracy of atrial fibrillation detection from wrist photoplethysmography. a study on post-operative patients - [Paper]
- 2019 - Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches - [Paper]
- 2019 - Detection of atrial fibrillation using a wrist-worn device - [Paper]
- 2019 - Diagnostic accuracy of an algorithm for detecting atrial fibrillation in a wrist‐type pulse wave monitor - [Paper]
- 2019 - How Accurately Can We Detect Atrial Fibrillation Using Photoplethysmography Data Measured in Daily Life? - [Paper]
- 2019 - Smartwatch Based Atrial Fibrillation Detection from Photoplethysmography Signals - [Paper]
- 2019 - Smartwatch PPG Peak Detection Method for Sinus Rhythm and Cardiac Arrhythmia - [Paper]
- 2019 - Wrist band photoplethysmography in detection of individual pulses in atrial fibrillation and algorithm-based detection of atrial fibrillation [Paper]
- 2020 - Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch - [Paper]
- 2020 - Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study - [Paper]
- 2021 - Continuous Heart Rate Monitoring for Automatic Detection of Life-Threatening Arrhythmias With Novel Bio-Sensing Technology - [Paper]
- 2021 - Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch - [Paper]
- 2021 - Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection - [Paper]
- 2022 - A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia - [Paper]
- 2022 - Accuracy of wristwatch-type photoplethysmography in detecting atrial fibrillation in daily life - [Paper]
- 2022 - Atrial fibrillation detection using ambulatory smartwatch photoplethysmography and validation with simultaneous holter recording - [Paper]
- 2022 - Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation - [Paper]
- 2022 - Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study - [Paper]
- 2016 - Reliable PPG-based algorithm in atrial fibrillation detection - [[Paper](Reliable PPG-based algorithm in atrial fibrillation detection)]
- 2016 - Wrist-located optical device for atrial fibrillation screening: A clinical study on twenty patients - [Paper]
- 2017 - Computationally Efficient Algorithm for Photoplethysmography-Based Atrial Fibrillation Detection Using Smartphones - [Paper]
- 2017 - Detection of atrial fibrillation episodes using a wristband device - [Paper]
- 2019 - Can one detect atrial fibrillation using a wrist-type photoplethysmographic device? - [Paper]
- 2019 - Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation - [Paper]
- 2019 - Photoplethysmography based Arrhythmia Detection and Classification - [Paper]
- 2019 - Using PPG Signals and Wearable Devices for Atrial Fibrillation Screening - [Paper]
- 2019 - Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study - [Paper]
- 2020 - A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor - [Paper]
- 2020 - Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification - [Paper]
- 2020 - Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window - [Paper]
- 2020 - Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data - [Paper]
- 2020 - Experimental comparison of photoplethysmography-based atrial fibrillation detection using simple machine learning methods - [Paper]
- 2020 - Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation - [Paper]
- 2020 - Population-Based Screening or Targeted Screening Based on Initial Clinical Risk Assessment for Atrial Fibrillation: A Report from the Huawei Heart Study - [Paper]
- 2021 - Classification of Atrial Fibrillation Based on Support Vector Machine - [Paper]
- 2021 - Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction - [Paper]
- 2022 - Atrial Fibrillation Detection and Atrial Fibrillation Burden Estimation via Wearables - [Paper]
- 2022 - Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study - [Paper]
- 2022 - Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches - [Paper]
- 2022 - Photoplethysmograph based arrhythmia detection using morphological features - [Paper]
- 2022 - Pulse Wave Analysis of Photoplethysmography Signals to Enhance Classification of Cardiac Arrhythmias - [Paper]
- 2023 - Evaluation of Atrial Fibrillation Detection in short-term Photoplethysmography (PPG) signals using artificial intelligence - [Paper]
- 2018 - Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning - [Paper]
- 2018 - Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study - [Paper]
- 2018 - Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor - [Paper]
- 2018 - Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks - [Paper]
- 2018 - Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms - [Paper]
- 2018 - Passive detection of atrial fibrillation using a commercially available smartwatch - [Paper]
- 2019 - Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning - [Paper]
- 2019 - Atrial Fibrillation Detection from PPG Interbeat Intervals via a Recurrent Neural Network - [Paper]
- 2019 - Atrial Fibrillation Detection from Wrist Photoplethysmography Data Using Artificial Neural Networks - [Paper]
- 2019 - Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study - [Paper]
- 2019 - End-to-end deep learning from raw sensor data: Atrial fibrillation detection using wearables - [Paper]
- 2019 - Photoplethysmography based Arrhythmia Detection and Classification - [Paper]
- 2020 - A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation - [Paper]
- 2020 - Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data - [Paper]
- 2020 - Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application - [Paper]
- 2020 - Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning - [Paper]
- 2020 - Challenging the Limitations of Atrial Fibrillation Detection in the Presence of Other Cardiac Arrythmias - [Paper]
- 2020 - Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study - [Paper]
- 2020 - Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study - [Paper]
- 2020 - Multi-task deep learning for cardiac rhythm detection in wearable devices - [Paper]
- 2021 - Atrial Fibrillation Classification with SmartWearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks - [Paper]
- 2021 - Towards Early Detection and Burden Estimation of Atrial Fibrillation in an Ambulatory Free-living Environment - [Paper]
- 2022 - Atrial Fibrillation Detection by Means of Edge Computing on Wearable device: a Feasibility Assessment - [Paper]
- 2022 - Atrial fibrillation detection in ambulatory patients using a smart ring powered by deep learning analysis of continuous photoplethysmography monitoring - [Paper]
- 2022 - BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data - [Paper]
- 2022 - Cluster consistency: Simple yet effect robust learning algorithm on large-scale photoplethysmography for atrial fibrillation detection in the presence of real-world label noise - [Paper]
- 2022 - Detecting Atrial Fibrillation in Real Time Based on PPG via Two CNNs for Quality Assessment and Detection - [Paper]
- 2022 - Dynamic time warping based arrhythmia detection using photoplethysmography signals - [Paper]
- 2022 - Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss - [Paper]
- 2022 - Motion-Robust Atrial Fibrillation Detection Based on Remote-Photoplethysmography - [Paper]
- 2022 - Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network - [Paper]
- Time-varying coherence function for atrial fibrillation detection - [Paper]
- Atrial fibrillation detection using photo-plethysmography and acceleration data at the wrist - [Paper]
- Monitoring and detecting atrial fibrillation using wearable technology - [Paper]
- PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application - [Paper]
- Reliable PPG-based algorithm in atrial fibrillation detection - [[Paper](Reliable PPG-based algorithm in atrial fibrillation detection)]
- Smart detection of atrial fibrillation - [Paper]
- Wrist-located optical device for atrial fibrillation screening: A clinical study on twenty patients - [Paper]
- A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology - [Paper]
- Computationally Efficient Algorithm for Photoplethysmography-Based Atrial Fibrillation Detection Using Smartphones - [Paper]
- Detection of atrial fibrillation episodes using a wristband device - [Paper]
- Detection of atrial fibrillation using an earlobe photoplethysmographic sensor - [Paper]
- Identification of atrial fibrillation by quantitative analyses of fingertip photoplethysmogram - [Paper]
- Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning - [Paper]
- Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions - [Paper]
- Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study - [Paper]
- Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor - [Paper]
- Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks - [Paper]
- Developing a novel noise artifact detection algorithm for smartphone PPG signals: preliminary results - [Paper]
- Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms - [Paper]
- Motion and noise artifact-resilient atrial fibrillation detection using a smartphone - [Paper]
- Passive detection of atrial fibrillation using a commercially available smartwatch - [Paper]
- The accuracy of atrial fibrillation detection from wrist photoplethysmography. a study on post-operative patients - [Paper]
- Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning - [Paper]
- Atrial Fibrillation Detection from PPG Interbeat Intervals via a Recurrent Neural Network - [Paper]
- Atrial Fibrillation Detection from Wrist Photoplethysmography Data Using Artificial Neural Networks - [Paper]
- Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches - [Paper]
- Can one detect atrial fibrillation using a wrist-type photoplethysmographic device? - [Paper]
- Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study - [Paper]
- Detection of atrial fibrillation using a wrist-worn device - [Paper]
- Diagnostic accuracy of an algorithm for detecting atrial fibrillation in a wrist‐type pulse wave monitor - [Paper]
- End-to-end deep learning from raw sensor data: Atrial fibrillation detection using wearables - [Paper]
- How Accurately Can We Detect Atrial Fibrillation Using Photoplethysmography Data Measured in Daily Life? - [Paper]
- Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation - [Paper]
- Photoplethysmography based Arrhythmia Detection and Classification - [Paper]
- Photoplethysmography based Arrhythmia Detection and Classification - [Paper]
- Smartwatch Based Atrial Fibrillation Detection from Photoplethysmography Signals - [Paper]
- Smartwatch PPG Peak Detection Method for Sinus Rhythm and Cardiac Arrhythmia - [Paper]
- Using PPG Signals and Wearable Devices for Atrial Fibrillation Screening - [Paper]
- Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study - [Paper]
- Wrist band photoplethysmography in detection of individual pulses in atrial fibrillation and algorithm-based detection of atrial fibrillation [Paper]
- A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor - [Paper]
- A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation - [Paper]
- Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification - [Paper]
- Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data - [Paper]
- Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application - [Paper]
- Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window - [Paper]
- Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning - [Paper]
- Challenging the Limitations of Atrial Fibrillation Detection in the Presence of Other Cardiac Arrythmias - [Paper]
- Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data - [Paper]
- Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study - [Paper]
- Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study - [Paper]
- Experimental comparison of photoplethysmography-based atrial fibrillation detection using simple machine learning methods - [Paper]
- Multi-task deep learning for cardiac rhythm detection in wearable devices - [Paper]
- Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation - [Paper]
- Population-Based Screening or Targeted Screening Based on Initial Clinical Risk Assessment for Atrial Fibrillation: A Report from the Huawei Heart Study - [Paper]
- Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch - [Paper]
- Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study - [Paper]
- Atrial Fibrillation Classification with SmartWearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks - [Paper]
- Classification of Atrial Fibrillation Based on Support Vector Machine - [Paper]
- Continuous Heart Rate Monitoring for Automatic Detection of Life-Threatening Arrhythmias With Novel Bio-Sensing Technology - [Paper]
- Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction - [Paper]
- Towards Early Detection and Burden Estimation of Atrial Fibrillation in an Ambulatory Free-living Environment - [Paper]
- Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch - [Paper]
- Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection - [Paper]
- A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia - [Paper]
- Accuracy of wristwatch-type photoplethysmography in detecting atrial fibrillation in daily life - [Paper]
- Atrial Fibrillation Detection and Atrial Fibrillation Burden Estimation via Wearables - [Paper]
- Atrial Fibrillation Detection by Means of Edge Computing on Wearable device: a Feasibility Assessment - [Paper]
- Atrial fibrillation detection in ambulatory patients using a smart ring powered by deep learning analysis of continuous photoplethysmography monitoring - [Paper]
- Atrial fibrillation detection using ambulatory smartwatch photoplethysmography and validation with simultaneous holter recording - [Paper]
- BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data - [Paper]
- Cluster consistency: Simple yet effect robust learning algorithm on large-scale photoplethysmography for atrial fibrillation detection in the presence of real-world label noise - [Paper]
- Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation - [Paper]
- Detecting Atrial Fibrillation in Real Time Based on PPG via Two CNNs for Quality Assessment and Detection - [Paper]
- Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study - [Paper]
- Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study - [Paper]
- Dynamic time warping based arrhythmia detection using photoplethysmography signals - [Paper]
- Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches - [Paper]
- Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss - [Paper]
- Motion-Robust Atrial Fibrillation Detection Based on Remote-Photoplethysmography - [Paper]
- Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network - [Paper]
- Photoplethysmograph based arrhythmia detection using morphological features - [Paper]
- Pulse Wave Analysis of Photoplethysmography Signals to Enhance Classification of Cardiac Arrhythmias - [Paper]
- Evaluation of Atrial Fibrillation Detection in short-term Photoplethysmography (PPG) signals using artificial intelligence - [Paper]
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