Dear Colleagues,
The fusion of artificial intelligence (AI) and biosignals has sparked a revolution in healthcare, driving breakthroughs across diagnostics, monitoring, and treatment. From wearable gadgets to medical sensors, biosignals provide a rich source of physiological data ripe for leveraging. Through the power of machine & deep learning, researchers are uncovering patterns from revolutionary biosignals in medicine. Combining AI's computational prowess with biosignal analysis, healthcare practitioners can gain deeper insights into patients' health statuses, enabling more accurate diagnoses and personalized treatment plans. Furthermore, AI-powered biosignal technologies offer the potential to enhance remote monitoring capabilities, improving patient outcomes and reducing healthcare costs.
This special issue aims to shed light on cutting-edge research at the nexus of AI and biosignal analysis, showcasing the transformative potential of this interdisciplinary field in revolutionizing healthcare delivery. Contributions may encompass a wide range of topics, including but not limited to:
Signal processing applications: spanning EEG, EMG, ECG, and evoked potential analysis.
Image processing applications: covering X-ray, PET, CT, MRI, and SPECT analysis.
Wearable applications: integrating biosignal monitoring into everyday devices to enhance health outcomes.
Author registration and submission: https://mc03.manuscriptcentral.com/jbiolmethods. Please submit your paper along with a cover letter including the special issue title. Your paper will undergo a fair peer review and be published immediately after acceptance and will be available to an international audience.
Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning