Machine Learning for Physiological Signal Analysis

Dear Colleagues,

Machine learning (ML) applications in physiological signal analysis are reshaping the process of interacting with technology, solving problems, and making decisions. Physiological signal analysis is a pivotal step in transforming raw signals into action-able and application-relevant information. Combining ML with innovative signal processing methodologies has significantly transformed conventional diagnostic and system design paradigms, enabling the extraction of subtle and hitherto inaccessible patterns from complex physiological time series. These intelligent, data-driven approaches are becoming a popular means in this scenario. Through analytical frameworks, facilitated by ML, software systems can automatically identify significant features and underlying trends, thereby providing clinicians and system designers with deeper insights and supporting more accurate, timely, and evidence-based decision-making in real-world applications.

In the past few years, ML has become the foundation of innovation across multiple key sectors. Blistering innovations in both physiological signal processing and ML algorithms have facilitated groundbreaking developments in various fields such as biometrics, medical data analytics, real-time health monitoring, and human machine interaction. Such application-oriented, data-driven methods provide not only enhanced efficiency and precision in physiological signal analysis but also do encourage more adaptive and intelligent computational methods. Consequently, ML keeps pushing the boundary of interpreting physiological signals and thus provides increasingly personalized, scalable, and predictive smart solutions.

This Special Issue focuses on showcasing recent developments at the intersection of signal processing and machine learning for physiological signal analysis. It seeks to unite quality original research contributions that address emerging theories, novel algorithms, innovative implementations and new applications of physiological signals and data analytics to the real-world.

Topics of interest include, but are not limited to, recent advances in machine learning algorithms to analyze physiological signals, intelligent processing frameworks, and sophisticated procedures to extract meaningful insights from physiological data.

• Physiological Signal Processing and Analysis for Healthcare;
• Physiological Signal Processing for Brain Computer Interface;
• Physiological Signal Processing for Human Machine Interaction;
• Physiological Signal Processing for Neural Rehabilitation Engineering;
• Physiological Signal Processing for Information Forensics and Security;
• Explainable and Trustworthy Machine Learning for Physiological Signal Analysis;
• Deep Learning Architectures for Multimodal Physiological Data Fusion;
• Edge AI and Embedded Machine Learning for Real-Time Physiological signal Processing;
• Federated and Privacy-Preserving Learning in Physiological Signal Processing;
• Self-Supervised and Few-Shot Learning for Physiological Signal Analysis;
• Physiological Signal Processing for Biometrics.

Dr. Saeed Mian Qaisar
Prof. Dr. Humaira Nisar
Prof. Dr. Abdulhamit Subasi
Guest Editors

Manuscript Submission Information

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