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Found 8 papers, 1 papers with code
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals
Self-AFNet managed to achieve an accuracy of 98. 07% and 98. 97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments.
Atrial Fibrillation Detection
Generative Adversarial Network
+1
A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN
It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0. 25% and 0. 27%, respectively.
Design, Implementation, and Performance Evaluation of a Fiber Bragg Gratings(FBG) based Smart Insole to Measure Plantar Pressure and Temperature
Various foot complications can be easily avoided by continuous monitoring of foot plantar temperature and pressure.
A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
Most existing methods used in the hospitals for continuous monitoring of BP are invasive.
QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds
Test subjects can simply download a mobile application, enter their symptoms, record an audio clip of their cough and breath, and upload the data anonymously to our servers.
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images.
Detection and severity classification of COVID-19 in CT images using deep learning
Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94. 13% and IoU of 91. 85% using the FPN model with the DenseNet201 encoder.
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks
This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals.
