Manuscript Topics

Biomedical signal processing is a foundation of contemporary healthcare, making possible the recovery of meaningful information from intricate physiological signals. With advancing health monitoring systems, incorporating sophisticated signal processing methods has become a necessity to enhance diagnostics, treatment and patient care. Next-generation health monitoring systems take advantage of advances in machine learning, artificial intelligence, wearable technologies and the Internet of Medical Things (IoMT) to enable continuous, real-time monitoring of biological signals like electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG) and others. Developments in deep learning, wearable devices and IoT technologies further enhance real-time monitoring and predictive capacity. Health monitoring encompasses biological systems, which are centered on human diagnostics and vital signs and physical systems which encompass infrastructure and machinery condition monitoring. Technologies such as handheld equipment, smart sensors and UAVs have revolutionized data gathering in both fields. Signal processing algorithms based on AI coupled with cloud platforms and high-speed computing, enable advanced analysis and accessibility. This slog advances the development of wearable sensors from the early Holter monitors through to flexible, skin-affordable devices capable of picking up multiple physiological and biochemical signals non-invasively. The application of nanotechnology metal, carbon and polymer nanomaterials has greatly improved sensor sensitivity, miniaturization and performance.

Quantum signal processing is another area that is being explored to speed up computation and enhance the validity of biomedical data analysis. While still in its nascent stage’s quantum algorithms can potentially revolutionize processing time for large-scale biomedical data, enabling real-time diagnosis and decision support. Besides algorithmic improvement, ethical issues of data security, patient confidentiality and algorithm explainability are equally important. Regulatory policies and strong encryption methods are being integrated into system designs for safeguarding sensitive health data. Explainable artificial intelligence models are in development to build clinician trust and regulatory acceptance. Future research directions include enhanced ECG and neural signal algorithms, brain-machine interfaces, multi-signal integration, and AI-driven diagnostics. Future frontiers include quantum signal processing, Internet of Medical Things (IoMT) and multimodal data fusion. Prosthetic innovations, mental health monitoring, telemedicine, and biofeedback systems are the driving factors behind adaptive, patient-centered healthcare solutions.

The Special Issue is dedicated to recent progress in biological signal processing and applications in healthcare monitoring and computer-aided diagnosis. It emphasizes the application of wearable and wireless sensors for recording multi-modal physiological signals and stresses the importance of sophisticated signal processing methods, such as time-frequency analysis, nonlinear analysis and entropy-based approaches. It also addresses the fusion of sensor data for holistic health monitoring and expanding applications of deep learning in medical image analysis, feature extraction and diagnostic decision support. The goal is to highlight state-of-the-art technologies that provide precise, real-time and intelligent healthcare solutions.

Contributions are invited on, but not restricted to, the following themes:

AI-Driven Biomedical Signal Processing for Real-Time Health Monitoring and Diagnostics
Quantum Algorithms for Accelerated Analysis of Physiological Signals in Healthcare
Deep Learning-Based Multi-Modal Biomedical Signal Fusion for Enhanced Patient Monitoring
Wearable Sensor Networks and Nonlinear Signal Processing in Continuous Health Surveillance
Internet of Medical Things (IoMT) Enabled Smart Biosignal Analytics for Personalized Medicine
Explainable AI Models for Transparent and Trustworthy Biomedical Signal Interpretation
Entropy-Based Feature Extraction Methods in Biomedical Signal Classification
Nanomaterial-Enhanced Flexible Sensors for High-Sensitivity Physiological Signal Acquisition
Cloud-Integrated Signal Processing Architectures for Scalable Healthcare Applications
Time-Frequency Analysis Techniques in ECG and EEG Signal Processing for Early Diagnosis
Machine Learning Approaches for Brain-Computer Interface Signal Enhancement and Decoding
Advanced Signal Processing Algorithms for Multi-Sensor Fusion in Telemedicine Systems
Ethical Frameworks and Data Security in AI-Enabled Biomedical Signal Processing

Instruction for Authors
http://www.aimspress.com/aimsmeds/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/