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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• physiological signals
• machine learning
• healthcare
• human–machine interactions
• brain–computer interface
• biometrics
• feature extraction
• multimodal data fusion
• explainable machine learning (XAI)
• real-time signal processing
• privacy-preservation