Emerging Trends in Deep Learning and Signal Processing for Wearable Biomedical Signal Analysis

Dear Colleagues,

In the domain of physiological signal processing, the integration of advanced signal processing methodologies across time, frequency, time-frequency, and non-linear domains has emerged as a pivotal area of research. This Special Issue aims to offer an interdisciplinary platform for the dissemination of innovative research, methodologies, and applications related to the analysis of complex physiological signals. The Issue is designed to explore and elucidate the application of cutting-edge signal processing techniques in the analysis of a spectrum of biomedical signals, encompassing electrodermal activity (EDA), electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), photoplethysmogram (PPG), as well as wearable sensor data and associated imaging modalities.

Moreover, the burgeoning synergy between deep learning algorithms in the domain of physiological signal classification, feature extraction, and predictive modeling has catalyzed advancements in terms of diagnostic and monitoring capabilities. This Special Issue aims to spotlight the advancements and challenges in the development and implementation of deep learning methodologies tailored for the analysis of physiological signals, with a specific emphasis on wearable sensor data. We warmly invite researchers, academics, and professionals to contribute their original research articles, comprehensive reviews, and concise communications, focusing on the latest innovations, methodological advancements, and future directions in the integration of advanced signal processing and deep learning paradigms for biomedical signal analysis, particularly in the context of wearable sensor technologies.

Dr. Hugo F. Posada-Quintero
Dr. Yedukondala Rao Veeranki
Guest Editors

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• physiological signal processing
• wearable sensors
• deep learning
• electrodermal activity
• electrocardiogram
• electromyogram
• electroencephalogram photoplethysmogram