Jiali Xie

Jiali Xie is a University Researcher at Eindhoven University of Technology, affiliated with the Electrical Engineering school and Signal Processing Systems department. They work within the Biomedical Diagnostics Lab and Eindhoven MedTech Innovation Center, focusing on medical signal processing applications.

Research spans obstructive sleep apnea diagnostics using multimodal signals (ECG, audio, respiratory effort) and medical deep learning systems. Key approaches include multi-task neural networks, convolutional architectures, and sensor fusion techniques for non-invasive monitoring. Recent work demonstrates strong applications in sleep staging, severity assessment, and real-world clinical implementation.

Their publication trends show concentrated focus on sleep-disordered breathing (87.5% of recent articles) with emerging contributions to remote sensing (25% of 2023 output). Methodologically, neural networks dominate (100% of medical publications) with emphasis on multi-modal data integration (66%) and frequency-scale analysis (44%).

Active in two major projects: MEDUSA (Medical Data Utilization Solutions Accelerator, 2020-2026) developing multimodal signal analysis for sleep apnea, and an earlier multimodal signal analysis project (2016-2021) focused on unobtrusive sleep apnea characterization. Media coverage includes expert commentary on building reconstruction methods from Guilin University of Technology.