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Influence of Finger Movement on the Stability of the Oscillometric Pulse Waveform for Blood Pressure Measurement

Published in 2021 Computing in Cardiology (CinC), Sep. 2021

In medical clinics the oscillometric automated sphygmomanometer is widely used. However, there are few studies to quantify the influence of the oscillometric pulse waveform stability on the accuracy of BP values. This study addresses this issue.

Recommended citation: Liu Jian, Alan Murray, Jianqing Li, and Chengyu Liu. "Influence of finger movement on the stability of the oscillometric pulse waveform for blood pressure measurement." In 2021 Computing in Cardiology (CinC), vol. 48, pp. 1-4. IEEE, 2021. DOI: 10.23919/CinC53138.2021.9662888.
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A Gaussian-Fitting-Optimized Algorithm for Blood Pressure Determination

Published in Biomedical Engineering Research, Jan. 2022

This Chinese-language work studies a Gaussian-fitting-optimized algorithm for blood pressure determination.

Recommended citation: Jian Liu, Yumin Li, Jianqing Li, and Chengyu Liu. "A Gaussian-Fitting-Optimized Algorithm for Blood Pressure Determination." Biomedical Engineering Research 2 (2022): 101-106. (in Chinese)

Sources of office blood pressure measurement error: A systematic review

Published in Physiological Measurement, Sep. 2022

A systematic review of all existing research on sources of OBPM errors. A search strategy was designed in six online databases, and all the literature published before October 2021 was selected. Those studies that used the OBPM device to measure BP from the upper arm of subjects were included.

Recommended citation: Liu Jian, Yumin Li, Jianqing Li, Dingchang Zheng, and Chengyu Liu. "Sources of automatic office blood pressure measurement error: a systematic review." Physiological Measurement 43, no. 9 (2022): 09TR02. DOI: 10.1088/1361-6579/ac890e.
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Personalized Transfer Learning for Single-Lead ECG-Based Sleep Apnea Detection: Exploring the Label Mapping Length and Transfer Strategy Using Hybrid Transformer Model

Published in IEEE Transactions on Instrumentation and Measurement, Jan. 2023

Two DL models, a pure convolutional neural network (CNN)-based model (PCM) and a proposed HTM, are included in the study. Eight different LMLs are considered. Furthermore, various personalized TL strategies are introduced to thoroughly explore the impact. Finally, two-sided t-tests are utilized to evaluate the significance.

Recommended citation: Hu Shuaicong, Yanan Wang, Liu Jian, and Cuiwei Yang. "Personalized Transfer Learning for Single-Lead ECG-Based Sleep Apnea Detection: Exploring the Label Mapping Length and Transfer Strategy Using Hybrid Transformer Model." IEEE Transactions on Instrumentation and Measurement (2023). DOI: 10.1109/TIM.2023.3312698.
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Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model

Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Jan. 2023

Two classical feature engineering methods and the proposed method which consists of various EEG rhythms are explored, then a hybrid Transformer model is designed to evaluate the advantages over pure convolutional neural networks (CNN)-based models. Finally, the performances of two model structures are analyzed utilizing patient-independent approach and two TL strategies.

Recommended citation: Hu Shuaicong, Liu Jian, Rui Yang, YaNan Wang, Aiguo Wang, Kuanzheng Li, Wenxin Liu, and Cuiwei Yang. "Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model." IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 (2023): 1321-1332. DOI: 10.1109/TNSRE.2023.3244045.
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A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals

Published in Biomedical Signal Processing and Control, Sep. 2023

In this paper we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilitie.

Recommended citation: Liu Jian, Shuaicong Hu, Zhijun Xiao, Qihan Hu, Daomiao Wang, and Cuiwei Yang. "A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals." Biomedical Signal Processing and Control 86 (2023): 105184. DOI: 10.1016/j.bspc.2023.105184.
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Semi-Supervised Learning for Low-Cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram

Published in IEEE Journal of Biomedical and Health Informatics, Nov. 2023

We utilize a convolutional neural network (CNN)-based auto-encoder (AE) with a modified training objective to detect anomalous region of OSA. An indicator based on model outputs is utilized as a benchmark measure to assign pseudo-labels with confidence to each sample. Finally, we perform validation of the semi-supervised algorithm on the same database and cross-database scenarios.

Recommended citation: Hu Shuaicong, Liu Jian, Cuiwei Yang, Aiguo Wang, Kuanzheng Li, and Wenxin Liu. "Semi-Supervised Learning for Low-Cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram." IEEE Journal of Biomedical and Health Informatics (2023). DOI: 10.1109/JBHI.2023.3304299.
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A Lightweight Hybrid Model Using Multiscale Markov Transition Field for Real-Time Quality Assessment of Photoplethysmography Signals

Published in IEEE Journal of Biomedical and Health Informatics, Feb. 2024

This study introduces a lightweight model to address the imperative need for precise, real-time evaluation of PPG signal quality, followed by its deployment and validation utilizing our integrated upper computer and hardware system

Recommended citation: Liu Jian, Shuaicong Hu, Qihan Hu, Daomiao Wang, and Cuiwei Yang. "A Lightweight Hybrid Model Using Multiscale Markov Transition Field for Real-Time Quality Assessment of Photoplethysmography Signals." IEEE Journal of Biomedical and Health Informatics (2024). DOI: 10.1109/JBHI.2023.3331975.
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A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer

Published in Computers in Biology and Medicine, Mar. 2024

This study proposes a multi-module heartbeat classification algorithm. Initially, unsupervised feature extractors are designed to extract rich features from unlabeled SD and TD data.

Recommended citation: Wang Yanan, Shuaicong Hu, Liu Jian, Gaoyan Zhong, and Cuiwei Yang. "A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer." Computers in Biology and Medicine 170 (2024): 108072.
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Preventing troublesome variability in clinical blood pressure measurement

Published in Journal of Human Hypertension, Nov. 2024

This study investigates the effect of six different measurement conditions (Quiet, Reading, Speaking, Deep Breathing, Moving, and Tapping) on BP readings in 30 healthy normotensive subjects. We hypothesize that non-standard conditions will result in significant deviations in BP measurements compared to the Quiet condition.

Recommended citation: Liu Chengyu, Liu Jian, Jianqing Li, and Alan Murray. "Preventing Troublesome Variability in Clinical Blood Pressure Measurement." Journal of Human Hypertension. (2024)
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Personalized Blood Pressure Estimation using Multiview Fusion Information of Wearable Physiological Signals and Transfer Learning

Published in Applied Soft Computing, Dec. 2024

In this paper we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilitie.

Recommended citation: Liu Jian, Shuaicong Hu, Yanan Wang, Wei Xiang, Qihan Hu, and Cuiwei Yang. "Personalized Blood Pressure Estimation using Multiview Fusion Information of Wearable Physiological Signals and Transfer Learning." Applied Soft Computing 167 (2024): 112390. DOI: 10.1016/j.asoc.2024.112390.
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Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

Published in Neural Networks, Jan. 2025

In this paper, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations.

Recommended citation: Hu Qihan, Daomiao Wang, Hong Wu, Liu Jian, and Cuiwei Yang. "Efficient multi-view fusion and flexible adaptation to view missing in cardiovascular system signals." Neural Networks 181 (2025): 106760. DOI: 10.1016/j.neunet.2024.106760.
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IPCT-Net: Parallel information bottleneck modality fusion network for obstructive sleep apnea diagnosis

Published in Neural Networks, Jan. 2025

This paper develops a modality fusion representation enhancement (MFRE) framework adaptable to flexible modality fusion types with the objective of improving OSA diagnostic performance, and providing quantitative evidence for clinical diagnostic modality selection.

Recommended citation: Hu Shuaicong, Yanan Wang, Liu Jian, Zhaoqiang Cui, Cuiwei Yang, Zhifeng Yao, and Junbo Ge. "IPCT-Net: Parallel information bottleneck modality fusion network for obstructive sleep apnea diagnosis." Neural Networks 181 (2025): 106836. DOI: 10.1016/j.neunet.2024.106836.
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An IoMT-Driven Framework for Precision Cardiovascular Assessment Incorporating Multiscale Perspectives and Microfiber Bragg Grating

Published in IEEE Internet of Things Journal, Feb. 2025

Herein, we introduce an innovative Internet of Medical Things (IoMT) framework for personalized hemodynamic assessment, driven by advanced flexible sensing technologies and multi-scale modeling.

Recommended citation: Liu Jian, Hengtian Zhu, Wei Xiang, Shuaicong Hu, Qihan Hu, Daomiao Wang, Huan Yang, Zhengyi Mao, Fei Xu, and Cuiwei Yang. "An IoMT-Driven Framework for Precision Cardiovascular Assessment Incorporating Multiscale Perspectives and Microfiber Bragg Grating." IEEE Internet of Things Journal (2025). DOI: 10.1109/JIOT.2024.3483450.
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LEAF-Net: A real-time fine-grained quality assessment system for physiological signals using lightweight evolutionary attention fusion

Published in Expert Systems with Applications, May. 2025

We have devised an attention network capable of simulating localized to global dependency relationships. By training individually, a corresponding QA model is assigned to each pattern. Fine-grained labels for the signals are allocated through weakly supervised learning. The proposed lightweight model has undergone systematic deployment, accompanied by the development of an interactive interface.

Recommended citation: Jian Liu, Shuaicong Hu, Yanan Wang, Qihan Hu, Daomiao Wang, Wei Xiang, Xujian Feng, and Cuiwei Yang. "LEAF-Net: A real-time fine-grained quality assessment system for physiological signals using lightweight evolutionary attention fusion." Expert Systems with Applications (2025). DOI: 10.1016/j.eswa.2025.126995.
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PULSE: A personalized physiological signal analysis framework via unsupervised domain adaptation and self-adaptive learning

Published in Expert Systems with Applications, Jun. 2025

PULSE is a personalized physiological signal analysis framework based on unsupervised domain adaptation and self-adaptive learning.

Recommended citation: Yanan Wang, Shuaicong Hu, Jian Liu, Aiguo Wang, Guohui Zhou, and Cuiwei Yang. "PULSE: A personalized physiological signal analysis framework via unsupervised domain adaptation and self-adaptive learning." Expert Systems with Applications 278 (2025): 127317. DOI: 10.1016/j.eswa.2025.127317.
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VAM: A Parallel Cross-Modal Hybrid Network for Accurate and Interpretable Vascular Age Estimation from PPG

Published in EMBC 2025, Jul. 2025

VAM is a parallel cross-modal hybrid network for accurate and interpretable vascular age estimation from photoplethysmography signals.

Recommended citation: Jian Liu, Shuaicong Hu, Yanan Wang, and Cuiwei Yang. "VAM: A Parallel Cross-Modal Hybrid Network for Accurate and Interpretable Vascular Age Estimation from PPG." EMBC 2025 (2025).

Transparent Artificial Intelligence-enabled Interpretable and Interactive Sleep Apnea Assessment across Flexible Monitoring Scenarios

Published in Nature Communications, Aug. 2025

This work presents transparent AI methods for interpretable and interactive sleep apnea assessment across flexible monitoring scenarios.

Recommended citation: Shuaicong Hu, Jian Liu, Yanan Wang, Cong Fu, Jichu Zhu, Huan Yu, and Cuiwei Yang. "Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios." Nature Communications (2025). DOI: 10.1038/s41467-025-62864-x.
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A Dual-Focus Cloud-Edge Collaborative Framework in Multitask Hemodynamic Parameter Cross-Scale Analysis: The Equilibrium of Clinical Performance and Efficiency

Published in IEEE Internet of Things Journal, Sep. 2025

This work presents a cloud-edge collaborative framework for multi-task hemodynamic parameter analysis, balancing clinical performance and computational efficiency.

Recommended citation: Jian Liu, Shuaicong Hu, Yanan Wang, Wei Xiang, and Cuiwei Yang. "A Dual-Focus Cloud-Edge Collaborative Framework in Multitask Hemodynamic Parameter Cross-Scale Analysis: The Equilibrium of Clinical Performance and Efficiency." IEEE Internet of Things Journal (2025). DOI: 10.1109/JIOT.2025.3579524.
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XSleepFusion: A Dual-stage Information Bottleneck Fusion Framework for Interpretable Multimodal Sleep Analysis

Published in Information Fusion, Nov. 2025

XSleepFusion is a dual-stage information bottleneck fusion framework for interpretable multimodal sleep analysis.

Recommended citation: Shuaicong Hu, Yanan Wang, Jian Liu, and Cuiwei Yang. "XSleepFusion: A Dual-stage Information Bottleneck Fusion Framework for Interpretable Multimodal Sleep Analysis." Information Fusion (2025): 103275. DOI: 10.1016/j.inffus.2025.103275.
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Unleashing the Power of Pretrained Transformer for Dense Prediction in Physiological Signals

Published in IEEE Journal of Biomedical and Health Informatics, Jan. 2026

This work studies pretrained transformers for dense prediction in physiological signals.

Recommended citation: Qihan Hu, Daomiao Wang, Hong Wu, Jian Liu, and Cuiwei Yang. "Unleashing the Power of Pretrained Transformer for Dense Prediction in Physiological Signals." IEEE Journal of Biomedical and Health Informatics 30, no. 1 (2026): 196-207. DOI: 10.1109/JBHI.2025.3592687.
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An Ultra-Efficient Edge-Based Wearable System for Real-Time and Remote Blood Pressure Monitoring

Published in Engineering Applications of Artificial Intelligence, Feb. 2026

This work presents an edge-based wearable system for real-time and remote blood pressure monitoring.

Recommended citation: Wei Xiang, Jian Liu, Shuaicong Hu, HaiHui Zhang, Chao Huang, and Cuiwei Yang. "An Ultra-Efficient Edge-Based Wearable System for Real-Time and Remote Blood Pressure Monitoring." Engineering Applications of Artificial Intelligence 166 (2026): 113603. DOI: 10.1016/j.engappai.2025.113603.
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Exploring an Adaptive Framework for Accurate Personalized Cuff-Less Blood Pressure Tracking

Published in IEEE Transactions on Instrumentation and Measurement, Feb. 2026

This work explores an adaptive framework for accurate personalized cuff-less blood pressure tracking.

Recommended citation: Yumin Li, Xiangyu Zhang, Li Ling, Jian Liu, Minghui Zhao, Chenxi Yang, Junjie Pan, Chaohong Liu, Huan Li, Wenkai Wang, Yanan Zhou, Chenghao Sui, Jianqing Li, and Chengyu Liu. "Exploring an Adaptive Framework for Accurate Personalized Cuff-Less Blood Pressure Tracking." IEEE Transactions on Instrumentation and Measurement (2026). DOI: 10.1109/TIM.2026.3659562.
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Edge-Intelligent Cross-Platform Architecture for Knowledge-Intensive Arterial Blood Pressure Inference in Distributed Healthcare IoT Networks

Published in Expert Systems with Applications, May. 2026

This work studies an edge-intelligent cross-platform architecture for arterial blood pressure inference in distributed healthcare IoT networks.

Recommended citation: Jian Liu, Shuaicong Hu, Yanan Wang, Wei Xiang, and Cuiwei Yang. "Edge-Intelligent Cross-Platform Architecture for Knowledge-Intensive Arterial Blood Pressure Inference in Distributed Healthcare IoT Networks." Expert Systems with Applications (2026).

Bridging the Gap Between Computer Vision and Bioelectrical Signal Analysis

Published in Information Fusion, May. 2026

This work studies connections between computer vision methods and bioelectrical signal analysis.

Recommended citation: Yanan Wang, Shuaicong Hu, Jian Liu, Aiguo Wang, Guohui Zhou, and Cuiwei Yang. "Bridging the Gap Between Computer Vision and Bioelectrical Signal Analysis." Information Fusion 129 (2026): 104047. DOI: 10.1016/j.inffus.2025.104047.
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Thinking like Clinicians: Ranking-based Multi-instance Learning for PPG-based Hemodynamic Fluctuation Detection

Published in Pattern Recognition, Jun. 2026

This work presents ranking-based multi-instance learning for PPG-based hemodynamic fluctuation detection.

Recommended citation: Daomiao Wang, Qihan Hu, Xiaoman Xing, Hong Wu, Jian Liu, Xuepeng Fu, and Cuiwei Yang. "Thinking like clinicians: Ranking-based multi-instance learning for PPG-based hemodynamic fluctuation detection." Pattern Recognition (2026). DOI: 10.1016/j.patcog.2025.112981.
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Why Specialist Models Still Matter: A Heterogeneous Multi-Agent Paradigm for Medical Artificial Intelligence

Published in International Conference on Machine Learning (ICML 2026), Jul. 2026

This work studies a heterogeneous multi-agent paradigm for medical artificial intelligence.

Recommended citation: Yanan Wang, Shuaicong Hu, Jian Liu, Guohui Zhou, Aiguo Wang, and Cuiwei Yang. "Why Specialist Models Still Matter: A Heterogeneous Multi-Agent Paradigm for Medical Artificial Intelligence." International Conference on Machine Learning (ICML 2026).

LSMOE

🌟 Advanced Hemodynamic Parameter Estimation via the Lightweight Sparse Multi-Gate Mixture-of-Experts (LSMOE) Framework for Complex Multi-Task Scenarios 🌟

LEAF-Net

🌟 A novel lightweight attention network designed to address the challenge of high-resolution quality assessment (QA) in multimodal data 🌟

FACT-Net

A Two-Stage Fusion-CNN-Transformer Framework for ABP Signal Reconstruction in Cross-Platform Multi-Patient IoT Healthcare Systems

2021 CINC ORAL

Published: September 15, 2021

More information here

Medical Electronic Instruments

, Jiangwan Campus, Fudan University, Teaching Building A, 2023

Course Schedule and Key Points 📚 Overview This course provides a comprehensive introduction to the characteristics, principles, and applications of medical instrumentation, with a focus on the measurement and processing of physiological signals such as bioelectric signals, neural activity, and cardiovascular metrics. The course combines foundational theories with practical applications and highlights advancements in both non-invasive and traditional measurement techniques. 🗓️ Schedule and Highlights September 15 Chapter 1: Characteristics of Medical Instruments Introduction to foundational concepts based on the textbook. September 22 Chapter 2: Measurement of Bioelectric Signals - Detection Part 1 Topics: Electrodes, Amplifiers, and Filtering Techniques. October 8 Assignment: Spectrum Analysis October 13 Chapter 2: Measurement of Bioelectric Signals - Detection Part 2 Topics: Sensors and Practical Applications. October 20 Chapter 2: Measurement of Bioelectric Signals - Processing Topics: Signal Processing Techniques, Noise Interference, and Suppression Methods. October 27 Chapter 2: Measurement of Bioelectric Signals - Electrocardiography (ECG) Topics: Principles of ECG and ECG Machines. November 3 Chapter 3: Neural System Measurements - Part 1 (EEG) Topics: Principles of Electroencephalography (EEG) (Lecture slides). November 10 Chapter 3: Neural System Measurements - Part 2 (EMG) Topics: Principles of Electromyography (EMG) (Lecture slides). November 16 Special Topic 1: Advances in Continuous Non-Invasive Blood Pressure Measurement Technologies Coverage of BCG, PPG, ICG, and related techniques. November 23 Assignment Submission: Spectrum Analysis November 30 Special Topic 2: Comparative Analysis of Non-Contact vs. Traditional ECG Collection Methods Discussion on Advantages and Limitations. 🎯 Key Learning Objectives Understand the basic characteristics and principles of medical instrumentation. Explore the detection and processing techniques for bioelectric signals, including electrodes, sensors, amplifiers, and noise suppression. Learn the principles and applications of neural measurement systems such as EEG and EMG. Gain insights into cutting-edge non-invasive measurement technologies, focusing on cardiovascular metrics like continuous blood pressure monitoring. Analyze the trade-offs between non-contact and traditional ECG techniques in clinical practice. This course integrates theoretical knowledge with real-world applications, fostering a deeper understanding of modern medical instrumentation. 📍 Venue: Jiangwan Campus, Fudan University, Teaching Building A 📅 Course Dates: September 1, 2023 - January 15, 2024 📍 Location: Shanghai, CN