Shuaicong Hu

Orcid: 0000-0001-5458-0416

According to our database1, Shuaicong Hu authored at least 11 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.
Comput. Biol. Medicine, March, 2024

A Lightweight Hybrid Model Using Multiscale Markov Transition Field for Real-Time Quality Assessment of Photoplethysmography Signals.
IEEE J. Biomed. Health Informatics, February, 2024

2023
Semi-Supervised Learning for Low-Cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram.
IEEE J. Biomed. Health Informatics, November, 2023

A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals.
Biomed. Signal Process. Control., September, 2023

Personalized Transfer Learning for Single-Lead ECG-Based Sleep Apnea Detection: Exploring the Label Mapping Length and Transfer Strategy Using Hybrid Transformer Model.
IEEE Trans. Instrum. Meas., 2023

CardiacSegFormer: Transformer for Semantic Segmentation of Cardiac Images.
Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition, 2023

DeT-Net: A Two-stage Method for Accurate Automatic Heart Segmentation.
Proceedings of the 16th International Congress on Image and Signal Processing, 2023

2022
A Hybrid Transformer Model for Obstructive Sleep Apnea Detection Based on Self-Attention Mechanism Using Single-Lead ECG.
IEEE Trans. Instrum. Meas., 2022

An automatic residual-constrained and clustering-boosting architecture for differentiated heartbeat classification.
Biomed. Signal Process. Control., 2022

2021
Analysis of ECG De-Noising Using Non-Local Means with Approximate Coefficients and Particle Swarm Optimization.
Proceedings of the 7th International Conference on Systems and Informatics, 2021

2020
Automatic 12-lead ECG Classification Using Deep Neural Networks.
Proceedings of the Computing in Cardiology, 2020


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