Multimodal Hemodynamic Monitoring, Data Analytics and Intelligent Modeling for Cardiovascular and Cerebrovascular Diseases

Dear Colleagues,

Cardiovascular and cerebrovascular diseases remain leading causes of mortality and disability worldwide. Their onset and progression involve complex interactions among neurocardiovascular functions. For example, during an orthostatic challenge such as standing up, a sudden drop in arterial blood pressure can reduce cerebral perfusion. To prevent fainting, the brain and cardiovascular system respond rapidly through dynamic cerebral autoregulation, baroreflex activation, heart-rate adjustments, and changes in vascular tone. This illustrates how cerebral hemodynamics and cardiac regulation operate as an integrated neurocardiovascular network.

With recent advances in multimodal physiological monitoring and medical imaging technologies, combined with artificial intelligence and data-driven computational methods, researchers are now able to investigate brain–heart interactions across structural, functional, hemodynamic, and metabolic dimensions. Integrated analysis of multimodal signals provides new opportunities to understand key pathophysiological mechanisms, such as impaired cerebral autoregulation, intracranial pressure fluctuations, neurocardiac dysregulation, vascular stenosis, and cerebral hypoperfusion, which underlie various cardiovascular and cerebrovascular conditions. These approaches have shown significant value in stroke management, cardiogenic brain injury, atherosclerosis, atrial fibrillation-related perfusion changes, perioperative monitoring, and continuous neuro-monitoring in critical care.

This Special Issue focuses on multimodal hemodynamic monitoring, signal processing, mathematical modeling, and artificial intelligence with applications in cardiovascular and cerebrovascular diseases. We welcome high-quality submissions, including original research and reviews.

Scope of the Special Issue

Topics include, but are not limited to, the following:

1. Time-Series Physiological Monitoring Technologies:

- Electroencephalography (EEG), including scalp EEG, ECoG, and SEEG/intracranial EEG;
- Transcranial ultrasound, including transcranial doppler (TCD), transcranial color-coded duplex (TCCD), and transcranial ultrasound imaging;
- Electrocardiographic monitoring (ECG), including multi-lead ECG, Holter monitoring, and HRV analysis;
- Near-infrared spectroscopy (NIRS), including CW-fNIRS, FD-NIRS, and TD-NIRS for cerebral blood flow and oxygenation;
- Continuous non-invasive blood pressure (cNIBP) and hemodynamic monitoring (CO, SVR, SV, and arterial stiffness);
- Polysomnography (EEG, EOG, EMG, airflow, respiratory effort, SpO2, pulse, and snoring signals);
- Development of related medical monitoring devices.

2. Medical Imaging and Biomedical Image Analysis:

- Cardiovascular and cerebrovascular image segmentation, reconstruction, and analysis;
- Multimodal fusion and AR technologies;
- Image-based robotic control for cardiovascular/neurovascular interventions;
- CFD modeling and hemodynamic simulation.

3. Artificial Intelligence and Clinical Data Analytics:

- Deep learning, time-series modeling, and predictive analytics.
- Multimodal ML for diagnosis, risk stratification, and prognosis.
- Reinforcement learning, large language models, and intelligent monitoring/decision support.

Prof. Dr. Jia Liu
Prof. Dr. Jing Qin
Dr. Chang Liu
Dr. Weitao Xu
Guest Editors

Dr. Jianhang Du
Guest Editor Assistant
Affiliation: The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
Website: https://orcid.org/0000-0001-9678-082X
E-mail: dujh8@mail.sysu.edu.cn
Interests: human hemodynamics; pulse-wave propagation modelling; multiscale biomechanical modelling; patient-specific 3D hemodynamic simulation; noninvasive circulatory support devices

Manuscript Submission Information

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• multimodal physiological monitoring
• biomedical signal and image processing
• computational human biomechanics
• artificial intelligence in biomedicine
• medical computing and robotics
• computer vision in medicine