Real-Time Analysis Of Biomedical Signals

Abstract

Internet of Thing (IOT) devices, non-invasive measurement techniques, and wearable sensors have led to a plethora of data in biomedical and healthcare systems. The appropriate utilization and analysis of such data can help clinicians and medical doctors make real-time decisions and save lives by transmitting early warning signals, predicting, monitoring, and diagnosing the condition of patients. Conversely, these measurement techniques are obviously subject to considerable noise effects resulting from sensor imperfections and with poor-quality sensor-patient contacts and subject to high uncertainty. There is therefore a considerable signal processing challenge involved in extracting reliably physiological state parameters for any patient type. The focus of this Special Issue is to gather original research and review papers investigating the signal and pattern processing potential in extracting physiological parameters using conventional and recent advances in principled signal processing. Of interest are signal processing techniques, machine learning, and intelligent approaches to process biomedical signals, pattern processing, emerging trend of measurements and analysis, and decision making.