Aligned with
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
This track focuses on the latest technologies and methodologies for acquiring biomedical signals, including ECG and EEG. Participants will explore innovations that enhance the quality and reliability of signal collection in clinical and research settings.
This session will delve into the development and application of predictive modeling techniques in biomedical contexts. Emphasis will be placed on supervised and unsupervised learning approaches to improve patient outcomes.
This track examines the integration of deep learning techniques in the analysis of biosignals. Discussions will include case studies demonstrating the effectiveness of these methods in real-world biomedical applications.
Focusing on the identification of anomalies in biomedical signals, this session will cover various algorithms and their applications. Participants will learn about the significance of early detection in improving patient care.
This track will explore advanced feature extraction methods tailored for biomedical signals. Participants will discuss how these techniques enhance the performance of predictive models in healthcare.
This session addresses the automation of workflows in biomedical signal processing and analysis. Attendees will learn about tools and frameworks that streamline processes and improve efficiency in research and clinical environments.
This track focuses on the importance of system monitoring and the evaluation of predictive models in biomedical applications. Participants will discuss best practices for ensuring model reliability and performance in dynamic healthcare settings.
This session explores the intersection of industrial IoT and biomedical signal processing. Discussions will highlight how IoT technologies can enhance data acquisition and real-time monitoring in healthcare.
This track examines the role of digital twin technologies in simulating and optimizing biomedical processes. Participants will explore case studies that demonstrate the potential of digital twins in predictive maintenance and process optimization.
Focusing on simulation techniques and analytics, this session will cover their applications in biomedical engineering. Participants will learn how these methods can drive innovation and improve decision-making in healthcare.
This track will delve into advanced techniques for processing neural signals, including EEG and other neurophysiological data. Participants will discuss the implications of these techniques for understanding brain function and developing therapeutic interventions.